Research fellow jobs in San Leandro, CA

Job Title: Research scientist (Robotics, AI) Position Type: Full-Time / Permanent Salary range: $200,000/yr - $270,000/yr Job Description: We are an early-stage robotics startup working on building multi-purpose mobile robots that can do complex manipulation tasks. We are looking for a creative, skilled, and motivated research scientists to join our founding team in advancing robot manipulation capabilities. We are looking for people with proven expertise in machine learning and/or robotics. You will collaborate with a team of talented researchers and engineers, and drive ongoing innovation and technological advancements within the company. This is a full-time on-site role in Santa Clara, CA. Responsibilities: Develop new algorithms and methods for training AI models for enhancing the robot dexterity. Conduct cutting edge research across multiple disciplines (Robotics, RL/IL, control, perception, LLM, VLM, etc.). Work with large-scale ML systems and large-scale model training/fine-tuning. Design and implement state-of-the-art learning-based manipulation/navigation/control algorithms on real robots. Work with other teams to develop a diverse set of robust manipulation skills for robots. Requirements: Ph.D. degree in Robotics, Computer Science/Engineering, Electrical Engineering, Mechanical Engineering, etc., or equivalent research experience. Passionate about working with robots and building robot products. Excellent analytical, problem-solving, and communication skills. At least 3 years of experience conducting independent research. Deep understanding of the SOTA robot learning techniques (reinforcement learning, imitation learning, etc.) A track record of research excellence with your work published in top conferences and journals such as Science Robotics, IJRR, RSS, CoRL, ICRA, NeurIPS, ICML, ICLR, CVPR, etc. Proficient with Python. Proficient with deep learning libraries such as PyTorch/TensorFlow/Jax. Experience with real robot experiments. Experienced with robot simulators such as Isaac Gym/ Isaac Sim/ SAPIEN/ MuJoCo/Drake, etc. About Us: Founded in 2009, IntelliPro stands as a global leader in talent acquisition and HR solutions. Our commitment to delivering unparalleled service to clients, fostering employee growth, and building enduring partnerships sets us apart. With a dynamic presence in the USA, China, Canada, Singapore, Philippines, UK, India, Netherlands, and Germany, we continue to lead the way in global talent solutions. IntelliPro is proud to be an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We also ensure that all applicants have access to accommodations throughout the hiring process. Learn more at *************************** Compensation: The compensation offered will depend on various factors, including location, experience, education, and job-related skills. This role includes a competitive base salary, bonus, equity, and a comprehensive benefits package, subject to eligibility.

Join a Leading Applied Research Lab Pushing the Boundaries of Reinforcement Learning Are you passionate about advancing the frontiers of reinforcement learning (RL)? An innovative AI research lab is seeking talented and ambitious scientists to shape the next generation of RL techniques-especially where they intersect with large models and environment generation. About the Role As an AI Research Scientist focused on RL, you will: Develop novel optimization-based methods for automated RL environment generation Establish baselines for evaluating the quality and diversity of RL environments Design infrastructure to create dynamic environments from historical datasets and agent evaluations Drive your own research agenda, contributing directly to the progress of our platform and the broader AI community What We're Looking For PhD (or equivalent experience) in machine learning, computer science or a related field Strong publication record and/or evidence of research impact (open source, deployed systems, etc.) Deep expertise in reinforcement learning and machine learning fundamentals Proficient in Python and at least one modern ML framework (such as PyTorch or JAX) Bonus Points Experience with post-training large language models (LLMs) Demonstrated software engineering skills Ability to communicate research findings effectively to both technical and non-technical audiences

⚡ Research Scientists/Engineers (all levels) 🔍 Focus on Vision Data Infrastructure 🤖 Fundamental AI Research Institute 🌎 San Francisco Bay Area, USA 💸 $250,000 - $600,000 salary + annual bonus Come join one of the only research institutions globally with resources to compete with top AI companies =>10s of 1000s of GPUs to explore state-of-the-art research in LLMs, Multimodal and Agentic AI. Currently seeking AI talent with expertise in building scalable pipelines for vision data to support both image/video generative training and multi-modal alignment. You'll design high-performance pipelines for large-scale image and video datasets, enabling efficient pretraining, alignment, and simulation-based data generation. Responsibilities: Vision Data Sourcing & Curation Collect and organize image and video data from open datasets and the web. Handle data cleaning, filtering, deduplication, and metadata generation. Ensure ethical and compliant data collection at scale. Processing & Augmentation Build high-throughput pipelines for vision data preprocessing (frame extraction, resolution normalization, format conversion, latent caching). Implement GPU-accelerated augmentation and distributed data loading (WebDataset, TFRecords, Parquet). Synthetic & Simulation-Based Data Generation Use simulation tools (e.g., Unreal Engine 5, Isaac Sim, Unity) to generate high-quality synthetic vision data. Create specialized datasets for VLM training, visual reasoning, and agent interaction. Requirements: Strong experience with data engineering, computer vision, or machine learning infrastructure. Expertise in building and scaling ETL/data pipelines for large unstructured datasets. Proficiency with Python, PyTorch, and distributed data frameworks (e.g., Ray, Spark, Dask). Experience with WebDataset, TFRecords, Parquet, or similar high-throughput data formats. Familiarity with GPU-accelerated preprocessing, NVIDIA DALI, or equivalent systems. Understanding of image/video codecs, data compression, and cloud storage optimization. Preferred Experience: Prior work with simulation-based or synthetic data generation using Unreal Engine, Isaac Sim, or Unity. Experience curating datasets for multimodal or vision-language model training. Knowledge of data ethics, privacy, and compliance frameworks for large-scale AI datasets. Experience contributing to open datasets or data-centric AI research. Why apply: Opportunity to join a fast-growing core team that are already pushing AI breakthroughs Highly competitive salary package Work alongside ambitious and bright superstars from tech and academia Medical, Dental and Vision Insurance Relocation package available 🌎 San Francisco Bay Area, USA 📧 Interested in applying? Please click on the ‘Easy Apply' button or alternatively email me your resume at ************************

We're working with a San Francisco client that's got a research team of 50~ professionals and looking to further expand it. They are specifically looking to flesh out their Research Group by hiring a Research Scientist on a hybrid basis. They have seen $300M in a recent fund raise. Skills targeted: PhD in Physics/ Quantum Physics/ Theory/ Statistics/ Mathematics/ Computational Science or similarly related field. 2+ YoE working with AI Agents. Good exposure to LLMs. Ideally a background in Audio focused research. Alternatively Search, would also be highly advantageous. TC package of $1-1,5M with sizeable base salary & equity package. If that looks of interest, apply & Goliath will be in touch!

Series A company looking to hire a Sr. Applied RS with a good background in multi-agent reinforcement learning. The related challenge is figuring out how to use feedback from an objective function on (consumer) pricing to reinforce a multi-agent enterprise system (many components). Qualifications: Applied Research with MS or PhD in a technical field and 4 + years relevant industry experience Successful track record as a self-starter in 0->1 scenarios with a proven ability to take SOTA models into production Please note: Due to the selective nature of this service and the volume of applicants we typically receive from our job postings, a follow-up email will not be sent until a match is identified with one of the startup investments in our portfolio. About Us: Greylock is an early-stage investor in hundreds of remarkable companies including Airbnb, LinkedIn, Dropbox, Workday, Cloudera, Facebook, Instagram, Roblox, Coinbase, Palo Alto Networks, among others. More can be found about us here: ********************* We are full-time, salaried employees of Greylock who provide free candidate referrals/introductions to our active investments to help them grow/succeed (as one of the many services we provide).

About Kyyba: Founded in 1998 and headquartered in Farmington Hills, MI, Kyyba has a global presence delivering high-quality resources and top-notch recruiting services, enabling businesses to effectively respond to organizational changes and technological advances. At Kyyba, the overall well-being of our employees and their families is important to us. We are proud of our work culture which embodies our core values; incorporating value, passion, excellence, empowerment, and happiness, creates a vibrant and productive atmosphere. We empower our employees with the resources, incentives, and flexibility that they need to support a healthy, balanced, and fulfilling career by providing many valuable benefits and a balanced compensation structure combined with career development. Mountain View, CA - Onsite Long Term Contract Position Description: This job will include 1) contributing to 3GPP standardization, 2) working on research projects for communication technologies for connected mobility, 3) contributing to other standardization, and 4) contributing to other research projects. Based on the phase of the work and the output, academic publications will be considered. PRIMARY RESPONSIBILITIES: The primary responsibilities will include, but are not limited to: Contribute to 3GPP standardization Create invention disclosures Write 3GPP RAN1, RAN2, and/or RAN contributions Attend and contribute to 3GPP RAN1, RAN2, and/or RAN standardization meetings Regularly report and present to the research team and management Propose and lead research projects for communication technologies for connected mobility Propose and conduct research and development with internal and external collaboration partners Create academic publications and invention disclosures Regularly report and present to the research team and management Contribute to other standardization (e.g., SAE, ETSI, IEEE 802) as requested Contribute to other research projects in TMNA R&D InfoTech Labs Requirements: THIS POSITION WILL BE WORKING FROM OUR MOUNTAIN VIEW CALIFORNIA SITE QUALIFICATIONS: Ph.D. holder in Electrical Engineering or Computer Science Being able to travel domestically and internationally for attending 3GPP standardization meetings. International travel will be typically to Asia (Japan, Korea, India) and Europe. There will be around 5 international or domestic travels per year (1 week travel each) 3GPP RAN1 or RAN2 delegate experience is required REQUIRED SKILLS: Research and development experience of wireless communications, preferably in vehicular environments Strong knowledge of wireless communication at Layer 1 and Layer 2 Solid understanding of 4G LTE and 5G NR standards and 3GPP standardization processes Experience of attending 3GPP meetings as a delegate and writing 3GPP contributions, preferably in RAN1 WG Capability to learn and explore new research areas Excellent time management and task prioritization skills Clear and concise presentation skills, both written and oral, with consideration for international audiences Demonstrated ability to work independently as well as within a highly motivated team environment Well-developed interpersonal and communication skills, including ability to respond professionally in all types of situations Disclaimer: Kyyba is an Equal Opportunity Employer. Kyyba does not discriminate on the basis of race, religion, color, sex, gender identity, sexual orientation, age, non-disqualifying physical or mental disability, national origin, veteran status or any other basis covered by appropriate law. Minorities / Females / Protected Veterans / Individuals with Disabilities are encouraged to apply. All employment is decided on the basis of qualifications, merit, and business need.” It is the policy of Kyyba to provide reasonable accommodation when requested by a qualified applicant or employee with a disability, unless such accommodation would cause an undue hardship. The policy regarding requests for reasonable accommodation applies to all aspects of employment, including the application process. If reasonable accommodation is needed, please contact Kyyba at ************ Rewards: Medical, dental, vision 401k Term life Voluntary life and disability insurance Optional Pre-paid legal plan Optional Identity theft plan Optional Medical and dependent FSA Work-visa sponsorship Opportunity for advancement Long-term assignment with opportunity for hire by client SELECT AWARDS An INC 5000 company for 10 years Corp! Michigan Economic Bright Spots Crain's Detroit Business Top Staffing Service Companies in Detroit TechServe Alliance Excellence Award- IT and Engineering Staffing & Solutions Best of MichBusiness winner in HR Wizards & Partnerships Metro Detroit Elite Category: Recruitment, Selection & Orientation for 101 Best & Brightest 101 Best & Brightest Companies to Work for in Michigan

About our team: Our team is dedicated to revolutionizing B2B e-commerce through the development of a groundbreaking AI search product. Leveraging cutting-edge LLM technology, we're creating a brand new AI search engine and streamline the entire purchasing process for B2B customers. Responsibilities ● Deliver full projects by defining the data structure, framework, design, and evaluation metrics for AI solution development and implementation. ● Develop and deploy Large Language Models (LLMs) to empower search and agent systems for a variety of applications. ● Identify new and upcoming product innovation areas by interacting with potential external and internal collaborators. ● Identify undefined problems in existing technology and engage stakeholders and leaders to address them. Qualifications ● PhD, Master's, or Bachelor's degree in Computer Science, Engineering, Mathematics, or related fields. ● Ability to implement and verify state-of-the-art NLP or Computer Vision algorithms. ● Experience in successfully applying machine learning approaches to real-world personalization problems. ● Experience with building large language models. ● Knowledge of statistical methods, with strong mathematical skills. ● Excellent problem-solving and programming skills in Python. The pay range for this position at commencement of employment is expected to be between $196,500/year and $325,500/year. However, base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If hired, employee will be in an “at-will position” and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors.

The University of California, San Francisco (UCSF) is dedicated to advancing health worldwide through innovative research, high-quality education, and exceptional patient care. UCSF is recognized for fostering collaboration among its renowned graduate schools, medical center, and research programs. The university is committed to excellence, driving breakthroughs that improve lives. UCSF serves as a hub for scientific discovery and education in the heart of San Francisco, CA. The position is in the group of Drs. Boris Bastian and Iwei Yeh at UCSF's Helen Diller Family Comprehensive Cancer Center, leaders in melanoma genetics and translational research. The lab offers a stimulating, collaborative environment with access to cutting-edge technologies and interdisciplinary expertise. Role Description This is a full-time, on-site role for a Postdoctoral Researcher based in the Melanoma Discovery Lab at the University of California, San Francisco (UCSF) is seeking a highly motivated postdoctoral fellow (MD or PhD) to study systemic immune responses to gene fusion-driven cancers. This project explores how potent oncogenic fusions can initiate neoplastic growth while simultaneously triggering immune surveillance, with Spitz tumors serving as a model. The broader aim is to understand how similar transient, immune-contained lesions may arise in other tissues and be eliminated before clinical detection. The fellow will join a collaborative effort between cancer geneticists/melanoma biologists and genetic immunologists, combining genomic and immunologic approaches to study the systemic response to fusion-driven neoplasms. Qualifications MD or PhD in cancer biology, immunology, genetics, or related fields Strong research background and publication record Experience with molecular, genomic, or immunologic methods preferred Intellectual curiosity about tumor-immune system interactions Proficiency in Laboratory Skills and experience in conducting hands-on experimental work Strong Research and Data Analysis capabilities with attention to detail Experience with publication in peer-reviewed journals is a plus To Apply Send a CV, brief statement of research interests, and contact information for three references to **********************.

Job Description Quantitative Geneticist, Predictive Breeding Time Type: Full Time The Opportunity At Ohalo, we are building the future of agriculture with our breakthrough Boosted breeding technology. We are seeking a visionary and hands-on Quantitative Geneticist to be a principal architect of the computational engine that drives our entire crop improvement strategy. This isn't a typical modeling role. You will be at the nexus of genetics, data science, and engineering, designing the predictive systems that guide our breeding decisions. You will build and deploy everything from genomic selection models to sophisticated simulations that chart the course of our breeding portfolio. If you are driven to solve complex problems and want to see your code and models directly translate into real-world genetic gain, this is a unique opportunity to make a foundational impact. Responsibilities As a key member of our technical team, your responsibilities will be organized around three core pillars: 1. Core Predictive Science Genomic Prediction & GWAS: Design, build, and validate the primary statistical models (e.g., GBLUP, ss GBLUP, GWAS) that form the foundation of our predictive capabilities, translating genotype and phenotype data into actionable insights. Breeding Simulation: Evolve our in-house breeding simulation platform to run complex, large-scale scenarios. Your models will answer critical strategic questions about resource allocation, risk management, and the optimal path to achieve our breeding objectives. 2. Strategic Decision Modeling Pipeline Optimization: Move beyond prediction to prescription. Design and implement online optimization models (e.g., using multi-armed bandits, online learning, metaheuristics) to create a self-improving system that dynamically allocates resources and maximizes the rate of genetic improvement. Portfolio Management & Utility: Develop and integrate multi-trait utility functions that align our selection strategy with market needs and product profiles. You will help manage the entire breeding portfolio as a strategic asset. 3. Innovation & Collaboration Accelerate Research with AI: Act as a force multiplier by leveraging modern AI tools across the research lifecycle. This includes using LLMs for hypothesis generation, pioneering the use of genomic foundation models (e.g., Evo2), and using AI-assisted tools to write, debug, and document production-quality code. Drive Cross-Functional Impact: Serve as a critical scientific partner to domain experts (breeders, plant scientists), Machine Learning Engineers (MLEs), and Data Engineers (DEs). Proactively translate breeding objectives into modeling requirements and ensure your solutions are seamlessly integrated into our operational workflows. Uphold Statistical Rigor: Collaborate with fellow quantitative scientists to champion statistical integrity across the organization, from experimental design to model validation and interpretation. Candidate Profile Education: M.S. or Ph.D. in Quantitative Genetics, Statistical Genetics, Plant Breeding, Biostatistics, Operations Research, or a related computational field. Core Experience: 2-5+ years of hands-on experience applying quantitative principles in a research or industry setting. A strong portfolio of projects demonstrating the application of predictive modeling and/or simulation is highly desired. Programming Excellence: Expert-level proficiency in Python and its scientific computing stack (e.g., NumPy, SciPy, Pandas, Scikit-learn). Demonstrable experience building modular, testable, and maintainable code is essential. Hands-on experience using generative AI tools (e.g., GitHub Copilot) to accelerate the development of scientific code. Statistical Modeling Expertise: Deep theoretical and practical understanding of mixed models for genetic evaluation (e.g., GBLUP, ss GBLUP). Proven experience with Bayesian statistics, applying methods such as Bayesian GBLUP, hierarchical models, and clustering using MCMC or variational inference. Familiarity with decision theory and online optimization frameworks (e.g., multi-armed bandits, Thompson sampling) for resource allocation. Experience with or interest in applying genomic foundation models (e.g., Evo2, other LLM-like architectures) to learn from large-scale sequence data. Experience with machine learning algorithms (e.g., XGBoost, Ridge Regression) as applied to genomic data. Collaboration & Communication: A proven ability to work effectively in a cross-functional team. You must be able to translate complex technical and scientific concepts for different audiences and work collaboratively to turn models into real-world impact. Genomic Data Acumen: Experience handling and processing large-scale genomic datasets (e.g., SNP arrays, sequencing data) is required. Bonus Points For: Proficiency in R, particularly for reading and translating legacy statistical models (e.g., brms, sommer, ASReml). Experience with workflow management tools (e.g., Nextflow, Snakemake). Familiarity with cloud computing environments (GCP, AWS) and data warehousing technologies (e.g., BigQuery). Knowledge of polyploid genetics and modeling. About Ohalo: Ohalo™ aims to accelerate evolution to unlock nature's potential. Founded in 2019, Ohalo develops novel breeding systems and improved plant varieties that help farmers grow more food with fewer natural resources, increasing the yield, resiliency, and genetic diversity of crops to sustainably feed our population. Ohalo's breakthrough technology, Boosted Breeding™, will usher in a new era of improved productivity to radically transform global agriculture. For more information, visit ************** The anticipated pay range for this role is $125,000 - $150,000 per year for our San Francisco, CA location, though salary will be based on a variety of factors including, but not limited to, experience, skills, education, and location. Notes: If you previously applied for a job at Ohalo, we encourage you to restate your interest in the position by submitting your application. No visa sponsorship is available for this position at this time. No recruiters please.

The Division of Medical Genetics in the Department of Pediatrics at Stanford University seeks a board certified/ eligible Medical Geneticist to join the Department and Division as an Assistant Professor, Associate Professor or Professor in the University Medical Line or as a Clinician Educator. Candidates must hold an MD or equivalent degree with board eligibility or board certification in medical genetics and genomics and have medical licensure in California by starting date. The major criteria for appointment for faculty in the University Medical Line shall be excellence in the overall mix of clinical care, clinical teaching, scholarly activity that advances clinical medicine, and institutional service appropriate to the programmatic need the individual is expected to fulfill. The major criterion for appointment as Clinician Educators is excellence in the overall mix of clinical care, teaching, administrative and/or scholarship appropriate to the programmatic need the individual is expected to fulfill. Faculty/Academic rank and line will be determined by the qualifications and experience of the successful candidate. We expect the successful candidate to participate in the care of patients under the care of the medical genetics service, biochemical genetics service and the perinatal genetics service. The candidate will be involved in formal and informal teaching of Medical Genetics residents, residents and fellows in other specialties, medical students and other graduate students. To be considered a candidate in the University Medical Line the successful candidate must have a focused research interest and a record or potential of scholarly accomplishment. For candidates holding certification in a specialty in addition to medical genetics, a secondary appointment in an additional Department may be possible. The activities of the Division/Department are diverse and include participation in the education of medical students, medical genetics residents and students in the Masters of Human Genetics and Genetic Counseling Program. Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. Stanford also welcomes applications from all who would bring additional dimensions to the University's research, teaching and clinical missions. Consistent with its obligations under the law, the University will provide reasonable accommodations to applicants and employees with disabilities. Applicants requiring a reasonable accommodation for any part of the application or hiring process should contact disability.access@stanford.edu . The university's central functions of research and education depend on freedom of thought, and expression. The Department of Pediatrics, School of Medicine, and Stanford University value faculty who will help foster an open and respectful academic environment for colleagues, students, and staff with a wide range of backgrounds, identities, and perspectives. Candidates may choose to include as part of their research and teaching statements a brief discussion about how their work and experience will further these values. How to apply: Please submit a CV and cover letter with an optional discussion of how your work and experience fosters additional dimensions to the university's mission and values. For questions, please contact: Dr. Michael Rosen, MD, MSCI, Search Chair c/o Stephanie Martinez (email: *********************) The expected base pay range for this position is: Assistant Professor Rank: $214,000-$227,000 Associate Professor Rank: $245,000-$258,000 Professor Rank: $291,000-$321,000 This pay range reflects base pay, which is based on faculty rank and years in rank. It does not include all components of the School of Medicine's faculty compensation program or pay from participation in departmental incentive compensation programs. For more information about compensation and our wide-range of benefits, including housing assistance, please contact the hiring department. Stanford University has provided a pay range representing its good faith estimate of what the university reasonably expects to pay for the position. The pay offered to the selected candidate will be determined based on factors including (but not limited to) the experience and qualifications of the selected candidate including equivalent years in rank, training, and field or discipline; internal equity; and external market pay for comparable jobs.

About the Company We are a seed-stage AI company building the industry standard for evaluating and benchmarking large language models on real enterprise tasks. About the Role As a Research Scientist, you will develop new benchmarks, methodologies, and evaluation pipelines that shape how cutting-edge models are assessed, compared, and deployed in production environments. Your work will directly influence model selection and safety decisions across foundation model labs, high-growth AI product companies, and Fortune-scale enterprises. Responsibilities Benchmarking & Model Analysis Evaluate newly released models as they launch (e.g., Gemini, DeepSeek, etc.) Run large-scale assessment workflows using internal evaluation infrastructure Compare model performance across enterprise-grade task categories Design New Benchmarks from Scratch Identify high-value model application domains through research exploration Construct datasets, including labeling strategy and workforce coordination Write short “white-paper-style” summaries explaining benchmark purpose, method, and findings Advance Automated Evaluation Methodologies Improve systems for scoring generated text beyond standard metrics Explore research in reference-free, rubric-based, and human-aligned evaluation Develop new techniques for reliability, consistency, and repeatability Cross-functional Collaboration Work closely with engineering to scale evaluation infra Partner with customers to refine evaluation relevance and task fit Influence product direction through research insights Qualifications 0-3 years post-grad experience (Master's, PhD, or equivalent applied research) Publications, preprints, or demos showing cutting-edge work Experience with diffusion models, NLP, multimodal, or benchmarking work Ability to operate independently with ambiguity Clean, maintainable research codebases Candidates from: top engineering/research universities applied AI startups well-regarded research internships Required Skills Built or contributed to a benchmark or evaluation methodology Experience in enterprise task model evaluation Stanford / top lab adjacency (per their historical hiring success) Preferred Skills Wants to publish as primary motivation Purely academic with slow timelines Big-tech culture fit concerns (Meta / Google / Salesforce specifically noted-but case-by-case) Pay range and compensation package Base Salary: up to $250K depending on background Equity: typically 0.3% - 0.5%, flexibility for exceptional candidates Rapid equity refresh possible based on impact Equal Opportunity Statement Visa sponsorship available. Relocation support. Health & dental coverage. Lunch + dinner provided, snacks & coffee. Unlimited PTO. Weekly happy hours with community guests. Team events (bowling, hiking, rock climbing, etc.). Swag program (hats, etc.). Work Environment & Culture In-person, San Francisco HQ (required). Core hours: 9-5, some teammates extend voluntarily. Most team members work 1 weekend day per week (flexible). High-ownership, low-ego, collaborative. Live demos Mondays, team lunch Thursdays, community Fridays. Early-stage pace, applied focus-not academic publishing. Tech Environment (while research-focused, exposure beneficial) Backend: Python / Django. Frontend: React + TypeScript. Infra: AWS. Evaluation frameworks + internal tooling. Why This Role Is Unique The company already collaborates with foundation model labs, high-growth AI vertical product companies, and Fortune 500 enterprises (not publicly facing). ChatGPT Vals AI $5M seed raised, runway of 2+ years at current burn. Only one research scientist is being hired-true founding impact. Opportunity to define industry standards for model trust, reliability, and certification. Positioned to become the rating agency for generative AI.

Join a Leading Applied Research Lab Pushing the Boundaries of Reinforcement Learning Are you passionate about advancing the frontiers of reinforcement learning (RL)? An innovative AI research lab is seeking talented and ambitious scientists to shape the next generation of RL techniques-especially where they intersect with large models and environment generation. About the Role As an AI Research Scientist focused on RL, you will: Develop novel optimization-based methods for automated RL environment generation Establish baselines for evaluating the quality and diversity of RL environments Design infrastructure to create dynamic environments from historical datasets and agent evaluations Drive your own research agenda, contributing directly to the progress of our platform and the broader AI community What We're Looking For PhD (or equivalent experience) in machine learning, computer science or a related field Strong publication record and/or evidence of research impact (open source, deployed systems, etc.) Deep expertise in reinforcement learning and machine learning fundamentals Proficient in Python and at least one modern ML framework (such as PyTorch or JAX) Bonus Points Experience with post-training large language models (LLMs) Demonstrated software engineering skills Ability to communicate research findings effectively to both technical and non-technical audiences

We're working with a San Francisco client that's got a research team of 50~ professionals and looking to further expand it. They are specifically looking to flesh out their Research Group by hiring a Research Scientist on a hybrid basis. They have seen $300M in a recent fund raise. Skills targeted: PhD in Physics/ Quantum Physics/ Theory/ Statistics/ Mathematics/ Computational Science or similarly related field. 2+ YoE working with AI Agents. Good exposure to LLMs. Ideally a background in Audio focused research. Alternatively Search, would also be highly advantageous. TC package of $1-1,5M with sizeable base salary & equity package. If that looks of interest, apply & Goliath will be in touch!

Early-stage (vertical RL) company looking to hire a Sr. Applied RS with a strong background in reinforcement learning. Qualifications: Applied Research with MS or PhD in a related field and 4 + years industry experience in deep learning and 2+ years with reinforcement learning Successful track record as a self-starter in 0->1 scenarios with a proven ability to take SOTA models into production Please note: Due to the selective nature of this service and the volume of applicants we typically receive from our job postings, a follow-up email will not be sent until a match is identified with one of the startup investments in our portfolio. About Us: Greylock is an early-stage investor in hundreds of remarkable companies including Airbnb, LinkedIn, Dropbox, Workday, Cloudera, Facebook, Instagram, Roblox, Coinbase, Palo Alto Networks, among others. More can be found about us here: ********************* We are full-time, salaried employees of Greylock who provide free candidate referrals/introductions to our active investments to help them grow/succeed (as one of the many services we provide). Please note: We are not recruiting for any roles within Greylock at this time. This job posting is for direct employment with a startup in our portfolio.

Research and Implementation of the research for future mobility services Developing innovative solutions that advance AI/ML systems for mobility services Conducting applied research in Agentic AI, including agent design, orchestration strategies, tool integration, and communication protocols Conducting research on AI safety and alignment topics Identifying research gaps and propose novel solutions to enhance performance, reliability, and adaptability of agent-based systems Prototyping frameworks and algorithms in Python using tools such as AutoGen, LangChain, or custom orchestration layers Required Qualifications: Ph.D. in Computer Science, Electrical Engineering, Mechanical Engineering, or a related engineering discipline with a focus on AI / Machine Learning Research experience in Generative AI, Agentic AI, and AI Safety and Alignment Research experience with Foundational (LLMs, VLMs, and other large-scale AI architectures), including fine-tuning, evaluation, and application to domain-specific tasks Proficient programming skills in Python and common libraries (e.g., TensorFlow, Pytorch, etc.) Ability to engage in general research activities such as defining problems and issues to be addressed, finding, and using research data, and being able to make recommendations and findings in writing and presentations

The Division of Medical Genetics in the Department of Pediatrics at Stanford University seeks a board certified/ eligible Medical Geneticist to join the Department and Division as an Assistant Professor, Associate Professor or Professor in the University Medical Line or as a Clinician Educator. Candidates must hold an MD or equivalent degree with board eligibility or board certification in medical genetics and genomics and have medical licensure in California by starting date. + The major criteria for appointment for faculty in the **University Medical Line** shall be excellence in the overall mix of clinical care, clinical teaching, scholarly activity that advances clinical medicine, and institutional service appropriate to the programmatic need the individual is expected to fulfill. + The major criterion for appointment as **Clinician Educators** is excellence in the overall mix of clinical care, teaching, administrative and/or scholarship appropriate to the programmatic need the individual is expected to fulfill. Faculty/Academic rank and line will be determined by the qualifications and experience of the successful candidate. We expect the successful candidate to participate in the care of patients under the care of the medical genetics service, biochemical genetics service and the perinatal genetics service. The candidate will be involved in formal and informal teaching of Medical Genetics residents, residents and fellows in other specialties, medical students and other graduate students. To be considered a candidate in the University Medical Line the successful candidate must have a focused research interest and a record or potential of scholarly accomplishment. For candidates holding certification in a specialty in addition to medical genetics, a secondary appointment in an additional Department may be possible. The activities of the Division/Department are diverse and include participation in the education of medical students, medical genetics residents and students in the Masters of Human Genetics and Genetic Counseling Program. _Stanford is an equal employment opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. Stanford also welcomes applications from all who would bring additional dimensions to the University's research, teaching and clinical missions._ _Consistent with its obligations under the law, the University will provide reasonable accommodations to applicants and employees with disabilities. Applicants requiring a reasonable accommodation for any part of the application or hiring process should contact_ _disability.access@stanford.edu_ _._ _The university's central functions of research and education depend on freedom of thought, and expression. The Department of Pediatrics, School of Medicine, and Stanford University value faculty who will help foster an open and respectful academic environment for colleagues, students, and staff with a wide range of backgrounds, identities, and perspectives. Candidates may choose to include as part of their research and teaching statements a brief discussion about how their work and experience will further these values._ **How to apply:** Please submit a CV and cover letter with an optional discussion of how your work and experience fosters additional dimensions to the university's mission and values. **For questions, please contact:** Dr. Michael Rosen, MD, MSCI, Search Chair c/o Stephanie Martinez (email: *********************) _The expected base pay range for this position is:_ _Assistant Professor Rank: $214,000-$227,000_ _Associate Professor Rank: $245,000-$258,000_ _Professor Rank: $291,000-$321,000_ This pay range reflects base pay, which is based on faculty rank and years in rank. It does not include all components of the School of Medicine's faculty compensation program or pay from participation in departmental incentive compensation programs. For more information about compensation and our wide-range of benefits (***************************************************** , including housing assistance (************************** , please contact the hiring department. Stanford University has provided a pay range representing its good faith estimate of what the university reasonably expects to pay for the position. The pay offered to the selected candidate will be determined based on factors including (but not limited to) the experience and qualifications of the selected candidate including equivalent years in rank, training, and field or discipline; internal equity; and external market pay for comparable jobs.

About the Company We are a seed-stage AI company building the industry standard for evaluating and benchmarking large language models on real enterprise tasks. About the Role As a Research Scientist, you will develop new benchmarks, methodologies, and evaluation pipelines that shape how cutting-edge models are assessed, compared, and deployed in production environments. Your work will directly influence model selection and safety decisions across foundation model labs, high-growth AI product companies, and Fortune-scale enterprises. Responsibilities Benchmarking & Model Analysis Evaluate newly released models as they launch (e.g., Gemini, DeepSeek, etc.) Run large-scale assessment workflows using internal evaluation infrastructure Compare model performance across enterprise-grade task categories Design New Benchmarks from Scratch Identify high-value model application domains through research exploration Construct datasets, including labeling strategy and workforce coordination Write short “white-paper-style” summaries explaining benchmark purpose, method, and findings Advance Automated Evaluation Methodologies Improve systems for scoring generated text beyond standard metrics Explore research in reference-free, rubric-based, and human-aligned evaluation Develop new techniques for reliability, consistency, and repeatability Cross-functional Collaboration Work closely with engineering to scale evaluation infra Partner with customers to refine evaluation relevance and task fit Influence product direction through research insights Qualifications 0-3 years post-grad experience (Master's, PhD, or equivalent applied research) Publications, preprints, or demos showing cutting-edge work Experience with diffusion models, NLP, multimodal, or benchmarking work Ability to operate independently with ambiguity Clean, maintainable research codebases Candidates from: top engineering/research universities applied AI startups well-regarded research internships Required Skills Built or contributed to a benchmark or evaluation methodology Experience in enterprise task model evaluation Stanford / top lab adjacency (per their historical hiring success) Preferred Skills Wants to publish as primary motivation Purely academic with slow timelines Big-tech culture fit concerns (Meta / Google / Salesforce specifically noted-but case-by-case) Pay range and compensation package Base Salary: up to $250K depending on background Equity: typically 0.3% - 0.5%, flexibility for exceptional candidates Rapid equity refresh possible based on impact Equal Opportunity Statement Visa sponsorship available. Relocation support. Health & dental coverage. Lunch + dinner provided, snacks & coffee. Unlimited PTO. Weekly happy hours with community guests. Team events (bowling, hiking, rock climbing, etc.). Swag program (hats, etc.). Work Environment & Culture In-person, San Francisco HQ (required). Core hours: 9-5, some teammates extend voluntarily. Most team members work 1 weekend day per week (flexible). High-ownership, low-ego, collaborative. Live demos Mondays, team lunch Thursdays, community Fridays. Early-stage pace, applied focus-not academic publishing. Tech Environment (while research-focused, exposure beneficial) Backend: Python / Django. Frontend: React + TypeScript. Infra: AWS. Evaluation frameworks + internal tooling. Why This Role Is Unique The company already collaborates with foundation model labs, high-growth AI vertical product companies, and Fortune 500 enterprises (not publicly facing). ChatGPT Vals AI $5M seed raised, runway of 2+ years at current burn. Only one research scientist is being hired-true founding impact. Opportunity to define industry standards for model trust, reliability, and certification. Positioned to become the rating agency for generative AI.

Join a Leading Applied Research Lab Pushing the Boundaries of Reinforcement Learning Are you passionate about advancing the frontiers of reinforcement learning (RL)? An innovative AI research lab is seeking talented and ambitious scientists to shape the next generation of RL techniques-especially where they intersect with large models and environment generation. About the Role As an AI Research Scientist focused on RL, you will: Develop novel optimization-based methods for automated RL environment generation Establish baselines for evaluating the quality and diversity of RL environments Design infrastructure to create dynamic environments from historical datasets and agent evaluations Drive your own research agenda, contributing directly to the progress of our platform and the broader AI community What We're Looking For PhD (or equivalent experience) in machine learning, computer science or a related field Strong publication record and/or evidence of research impact (open source, deployed systems, etc.) Deep expertise in reinforcement learning and machine learning fundamentals Proficient in Python and at least one modern ML framework (such as PyTorch or JAX) Bonus Points Experience with post-training large language models (LLMs) Demonstrated software engineering skills Ability to communicate research findings effectively to both technical and non-technical audiences

We're working with a San Francisco client that's got a research team of 50~ professionals and looking to further expand it. They are specifically looking to flesh out their Research Group by hiring a Research Scientist on a hybrid basis. They have seen $300M in a recent fund raise. Skills targeted: PhD in Physics/ Quantum Physics/ Theory/ Statistics/ Mathematics/ Computational Science or similarly related field. 2+ YoE working with AI Agents. Good exposure to LLMs. Ideally a background in Audio focused research. Alternatively Search, would also be highly advantageous. TC package of $1-1,5M with sizeable base salary & equity package. If that looks of interest, apply & Goliath will be in touch!