Postdoctoral scholar jobs in California - 3,019 jobs

### As a research scientist with Martian, you will develop new techniques to understand how AI models work. This work will focus on exploring and improving a technique we call “model mapping”: converting transformers into more interpretable representations (such as programs). We are looking for people who can develop and scale up methods for making transformers more interpretable through model mapping and then understanding the transformers in the new domain we map to. Full details can be found here. #J-18808-Ljbffr

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Sentra is building organizational superintelligence through memory infrastructure that reasons across time, causality, and context. As a Research Scientist, you will tackle fundamental problems in knowledge representation, temporal reasoning, and semantic compression. You will design and implement systems that maintain execution state for entire organizations, consolidate millions of micro-events into durable knowledge, and learn patterns that predict events before it happens. Key Responsibilities Build LLM-powered information extraction pipelines that process unstructured communications and text data into structured entity-relationship representations. Develop memory consolidation algorithms that validate information through multiple observations, merge duplicate entities, and prune ephemeral data. Design temporal knowledge graph architectures that model organizational execution state as living, continuously updated systems rather than static records. Create graph attention mechanisms and reasoning systems for complex causal queries about blockers, dependencies, and outcome patterns. Research lossy semantic compression using information-theoretic principles to condense event streams into query-relevant long-term memory. Design entity resolution systems handling identity evolution where entities merge, split, and transform through time. Build meta-learning systems that identify organizational patterns and recognize when current situations match historical success or failure indicators. Develop privacy-preserving cross-organizational learning using federated learning and differential privacy techniques. Publish research findings and contribute to the broader research community on knowledge graphs and organizational intelligence. Must-have Requirements 5+ years building novel systems in machine learning, NLP, knowledge graphs, or related areas with evidence through publications, production implementations, or significant open-source contributions. Deep knowledge of knowledge graphs, graph neural networks, or temporal reasoning demonstrated through shipped systems and architectural exploration. Strong ML and NLP foundation, particularly in information extraction, entity resolution, or semantic representation. Proficiency in Python and modern ML frameworks (PyTorch preferred) with experience deploying models at scale. Track record of publishing research (conference papers, technical blog posts, or detailed technical documentation) and exploring novel architectures. Ability to move between theoretical investigation and practical implementation, shipping research into production. Bonus skills: Graph databases (Neo4j, TigerGraph, Neptune) and query optimization for large-scale graphs. Information theory, compression, or temporal data structures. Causal inference, probabilistic reasoning, or Bayesian methods. Distributed systems, stream processing, or real-time ML serving. Human memory and cognition models. Privacy-preserving ML (federated learning, differential privacy, secure multi-party computation). Enterprise AI systems, workflow automation, or organizational software. Publications at top-tier conferences (NeurIPS, ICML, ICLR, KDD, EMNLP, ACL, WWW, SOSP, OSDI). Compensation and Benefits Base Salary: $150,000 - $300,000 Equity: 0.3% - 2% depending on level Comprehensive Health Coverage: Medical, dental, and vision Wellness & Productivity Stipend: $2,500/month to cover meals, transport, gym memberships, or other personal productivity needs Hardware & Tools: Latest MacBook Pro and AI development tools (ChatGPT Pro, Claude Pro, Cursor, etc.) Learning & Growth: Dedicated budget for conferences, courses, and professional development Relocation Support: Available for on-site hires Flexible Time Off Policy Total estimated annual benefits package: ~$30K-$35K in addition to base and equity. #J-18808-Ljbffr

A leading AI robotics firm based in San Francisco is seeking an AI Robotics Research Scientist. This role involves developing and training robotic AI models and working with an all-star team to tackle advanced robotics challenges. The ideal candidate will have a Ph.D. in Robotics or Computer Science and experience in deep learning and robotic hardware. A competitive salary range of $180,000 - $250,000 plus equity is offered, along with strong benefits that include paid time off, health insurance, and 401k contributions. #J-18808-Ljbffr

About us Our mission is to build causal intelligence, starting with physics models to predict and control the weather. We're building a small team driven by a deep passion and urgency to solve this civilizationally important problem. Our founding team has led & shipped models across self-driving cars, humanoid robotics, protein folding, and video generation at world-class institutions including Google DeepMind, Cruise, Waymo, Meta, Nabla Bio, and Apple. Responsibilities Work across the full ML stack (data, model, eval, and infrastructure) Implement novel model architectures and training algorithms Build data pipelines and training infrastructure for massive, petabyte-scale, multimodal datasets Rapidly iterate on experiments and ablations Stay up-to-date on research to bring new ideas to work What we're looking for We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains. Strong grasp of machine learning fundamentals, and depth in at least one core domain (e.g. Computer Vision, Sensor Fusion, Language Models, Physics-informed NNs) Experienced at training models and understanding experiment results through careful analysis and ablation studies. Experienced at writing and optimizing massive petabyte-scale data pipelines. Familiarity with distributed training and inference. [bonus] Familiarity with meteorology, computational fluid dynamics, and/or numerical simulations. You don't have to meet every single requirement above. Benefits Work on deeply challenging, unsolved problems Competitive cash and equity compensation Medical, dental, and vision insurance Catered lunch & dinner Unlimited paid time off Visa sponsorship & relocation support #J-18808-Ljbffr

Prima Mente's goal is to deeply understand the brain, to protect the brain from neurological disease and enhance the brain in health. We do this by generating our own data, building brain foundation models, and translating discovery to real clinical and research impact. Role focus As a Machine Learning Researcher, you will help design, train, and evaluate foundation models that learn from large-scale biological data (genomics, epigenomics, single-cell, proteomics, clinical signals). Depending on your strengths, you might skew more towards: Modelling & algorithms - new architectures, training objectives, scaling strategies, multi-task / multi-modal learning. Applied research - framing high-impact questions with clinicians and biologists, building end-to-end disease models, and stress-testing them on real data. Analysis & insight - probing model internals, interpretability, mechanistic understanding, biomarker discovery. Systems & efficiency - if you enjoy it, helping push training, data, and inference infrastructure to the next scale. The role is deliberately broad: we're looking for exceptional ML talent with strong research instincts, not a single CV template. What you'll work on You won't do all of these on day one; think of this as the space of things you may own. Design and implement ML models for large-scale biological data, from pre-training to task-specific fine tuning. Partner with biologists, clinicians, and data scientists to translate biological and clinical questions into tractable ML problems. Run end-to-end experiments: dataset curation, training, evaluation, error analysis, and iteration. Develop and refine evaluation suites for robustness, generalisation, and clinical relevance (e.g. across cohorts, sites, populations). Explore multi-modal and multi-task training across genomic, epigenomic, transcriptomic, proteomic and clinical signals. Perform in-depth model analysis to extract mechanistic or biomarker-level insights, not just metrics. Collaborate on papers, internal memos, and external communication of key research results. (Optional / plus) Contribute to scaling and optimisation of training and data pipelines, in close collaboration with research engineers. Expected Growth This is illustrative; we know great people ramp differently. 1 month: You've reproduced key baselines, run initial experiments on our internal datasets, and are comfortable with our training stack. You've shipped your first improvements (e.g. better objective, data pre-processing, or evaluation variant) and presented results to the team. 3 months: You own a research thread: a model family, disease application, or methodological idea. You're independently designing experiments, refining hypotheses, and coordinating with relevant partners (ML, wet lab, clinical). 6 months: You've delivered meaningful research impact: a stronger model, a new capability, a better biomarker, or evidence that changes our direction. You are a go-to person for your area, helping others design experiments, debug models, and evaluate results. Why Join Us: Direct patient impact: Your work sits on the critical path to earlier detection and better treatment of devastating brain diseases. End-to-end environment: We run the full stack from data generation to models to clinical studies, giving you an unusually tight feedback loop. Exceptional peers: You'll work with a small, high-calibre team across ML, biology, and clinical medicine. High autonomy, high bar: You'll have genuine ownership over problems that matter, with the expectation of operating at a very high standard. Who You Are You likely recognise yourself in several of these: Motivated by advancing human health through AI, especially in neuroscience and complex disease. Deeply curious, with a habit of reading papers, prototyping ideas, and stress-testing your own assumptions. Comfortable doing real engineering work in service of research - but see yourself first and foremost as a researcher. Enjoy collaborating across disciplines and explaining your work to people with very different backgrounds. Able to stay with hard problems for a long time, and to make progress even when the path isn't obvious. Ideal experience We don't expect you to check every box. Strong applicants often have depth in some of these and interest in growing into others. Strong background in machine learning or a closely-related field (e.g. deep learning, statistics, optimisation). Industry, academic, or hybrid paths are all welcome. Demonstrated experience training and evaluating modern ML models (e.g. transformers, diffusion, graph models, sequence models). Solid software skills in Python and at least one major ML framework (PyTorch, JAX, or TensorFlow). Experience designing and running non-trivial experiments: controlling for confounders, building robust baselines, and doing thorough error analysis. Ability to write clearly - whether in code comments, research docs, or papers. At least one of the following (more is a plus, not a requirement): Experience with large-scale data (e.g. 100B+ tokens or equivalent) or distributed training. Background in computational biology, genomics, epigenomics, neuroscience, or related areas. Work on foundation models (language, vision, or multi-modal) and interest in applying that to biology. Infra/optimisation experience (e.g. FSDP/ZeRO, quantisation, compilation, custom kernels) - especially valuable, but not mandatory. If you're unsure whether you “count” as an engineer or a researcher: please apply. We care about what you can do and how you think, not your current job title. Location Based in San Francisco, US or London, UK. We support visa applications. Culture Insight What we are doing is extremely hard. Prima Mente is for great people. We are team players who appreciate challenges, want to be hands-on, and thrive on curiosity by throwing away assumptions. We are focused on excellence at pace and huge personal growth. We are strong communicators who are highly disciplined and rigorous. Prima Mente operates with a flat organizational structure. We gain and share knowledge by contributing to multiple opportunities. Leadership is given to those who show initiative and consistently deliver excellence. We arrange our lives so we can work in person as much as possible. Our ValuesExceptional performance at exceptional pace The solutions we build demand uncompromising quality and rigour. The problems we are solving are grave and present. Inquisitive discovery We embrace curiosity and creativity. Every question is a path to a transformational breakthrough. Radical candour We practice unwavering honesty and transparency in all our challenges and interactions. Purposeful individuality Every individual in our team is celebrated for their identity, uniqueness, and experiences. We are invested in each one's bespoke personal development. Nurturing individuality will supercharge our collective purpose and spirit. Patient impact at scale We have a steadfast commitment to improve the health and well-being of patients globally. Every experiment run, every dataset analysed, and every innovation developed, is a step towards achieving a scalable impact. #J-18808-Ljbffr

A technology company based in San Francisco is seeking a talented Machine Learning Researcher to join its R&D team. The role focuses on developing advanced machine learning models for EEG-based neural decoding, collaborating with experts in the field, and contributing to high-impact research publications. The ideal candidate holds a significant background in ML research or engineering, particularly with deep learning, and proficiency with tools like Python and PyTorch. This position offers a competitive salary and benefits, including visa sponsorship and housing support. #J-18808-Ljbffr

A cutting-edge technology company in California seeks a Site Reliability Storage Engineer to design and operate scalable storage systems for AI research. The ideal candidate will have strong programming skills in Rust or Go, and experience with IaC tools and Kubernetes. This role offers a competitive salary ranging from $180,000 to $440,000 and comprehensive benefits including equity and health insurance. #J-18808-Ljbffr

About Nudge Nudge's goal is to help the brain work better by creating a generalized product that can precisely stimulate and image the brain, entirely non-invasively. We aim to achieve this by developing cutting-edge ultrasound technology to treat neurological and psychiatric disorders, like addiction, and eventually, creating a mainstream consumer device that can modulate mood, focus, sleep, and more. To realize those ambitions, we're growing a scrappy, multidisciplinary, science and engineering team focused on making the best technology possible for interfacing with the whole brain, and a product that has the potential to improve people's daily lives more than any other. About the team The Research Team at Nudge is innovating on multiple frontiers in neuroscience and neurotechnology, and is primarily focused on developing ultrasound-based neuromodulation to treat conditions like depression, chronic pain and addiction as well as enhance the wellbeing of healthy people. We're also building the first scalable, portable hardware for noninvasive structural ultrasound-based imaging the quality of an MRI. The team is running human trials for neuromodulation and developing algorithms that will enable on-device imaging. About the role Work closely with the Head of Research and engineering team to build the capabilities for on-device ultrasound-based imaging. Develop algorithms for structural and functional ultrasound-based imaging Perform validation of in-silico ultrasound imaging methods with Nudge hardware in physical models and in vivo Contribute to the development of aberration correction techniques informed by ultrasound-based imaging About you 5+ years experience PhD or Postdoc in Bioengineering, Medical Imaging, Physics or equivalent Expertise in ultrasound physics and metrology Demonstrated history of exceptional contributions in your prior work experiences Expertise in one or more of: Full-waveform inversion imaging Functional transcranial doppler imaging Functional ultrasound imaging Machine learning experience a plus Compensation $140,000 - $230,000/year + equity Your salary may vary depending on multiple factors such as job-related knowledge, skills, and experience. #J-18808-Ljbffr

At Doe, we're building an AI workforce that operates mission-critical workflows across private equity-backed rollups - starting with DSOs. These agents need to be fast, resilient, auditable, and secure. Here's why we might not be the right fit for you: We work hard and have a high-velocity environment with lots of growth opportunities. We value exceptional performance and continuous improvement. We believe that if you aren't constantly learning, you aren't growing. You will be responsible and accountable for making high-impact decisions that determine Who we're looking for: Someone who has a deep understanding of the ML development cycle, focusing on iteration speed and identifying bottlenecks Someone who is goal oriented and driven. You must be comfortable being given a goal and doing whatever is necessary to achieve the goal Profficient in Python, Tensorflow or PyTorch #J-18808-Ljbffr

About the Team The Strategic Deployment team makes frontier models more capable, reliable, and aligned to transform high-impact domains. On one hand, this involves deploying models in real-world, high-stakes settings to drive AI-driven transformation and elicit insights-training data, evaluation methods, and techniques-to shape our frontier model development. On the other hand, we leverage these learnings to build the science and engineering of impactful frontier model deployment. As a key element of this effort, OpenAI for Science aims to harness AI to accelerate the process of scientific research. This involves building models and an AI-powered platform that speeds up discovery and helps researchers everywhere do more, faster. About the Role As a Research Scientist focused on the mathematical sciences, you will help build models, tools, and workflows that move theoretical research-in fields such as mathematics, theoretical physics, and theoretical computer science-forward. You\'ll design domain-specific data and signals, shape training and evaluation, guide how to wire models to scientific tools, and work with the academic community to speed up adoption and impact. We\'re looking for people who... Hold a current or recent academic position in mathematical sciences (mathematics, theoretical physics, theoretical computer science) or a related field Regularly use frontier models in their own research Move easily between theory and code, and are eager to contribute technically as well as academically Either know or are eager to learn modern AI and run AI experiments end-to-end Are strong scientific communicators Care about rigor and reproducibility in scientific results This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. In this role, you will Assist in designing and building frontier AI models that are great at solving frontier mathematical sciences problems Build high-quality scientific datasets and synthetic data pipelines (symbolic, numeric, and simulator-based) Design reinforcement and grading signals for physics and run reinforcement learning/optimization loops to improve model reasoning Define and run evals for scientific reasoning, derivations, simulations, and literature grounding; track progress over time Partner with research labs and the academic community Drive adoption of frontier AI within the scientific community Uphold high standards for safety, data governance, and reproducibility You might thrive in this role if you Are passionate about pushing the boundaries of your field using AI Have used ChatGPT to do calculations and prove or improve lemmas in your field of study Communicate clearly to both scientists and AI engineers; you like collaborating across teams and with academia Nice to have Open-source contributions to mathematical science or AI tooling Experience building or curating domain datasets and benchmarks Experience engaging a research community (teaching, workshops, tutorials, standards) About OpenAI OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI\'s Affirmative Action and Equal Employment Opportunity Policy Statement. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations. To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link. OpenAI Global Applicant Privacy Policy At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology. Compensation Range: $380K - $460K #J-18808-Ljbffr

A cutting-edge robotics startup is looking for a Research Scientist to enhance their robotic manipulation systems. This role entails advancing research, collaborating on publications, and prototyping solutions within a multi-disciplinary team. The ideal candidate has a strong grasp of modern robotic methods and 5+ years of relevant experience, alongside a solid publication record in prestigious conferences. Compensation ranges from $160,000 to $210,000 annually, with equity options available. #J-18808-Ljbffr

A leading research institution in San Francisco seeks two PhD students to work on innovative machine learning projects focused on radiological precision medicine. Candidates will develop AI models for breast cancer risk prediction and assess equity in AI-supported screening methods. These positions offer a unique opportunity to contribute to groundbreaking medical research and work under prestigious supervisors. Applicants should hold a relevant master's degree and demonstrate proficiency in quantitative analysis and Python. #J-18808-Ljbffr

A pioneering AI company in San Francisco is seeking a talented AI Researcher to push the boundaries of custom AI research and model training. You will lead experiments in model architectures and fine-tune techniques for efficiency, collaborating with a skilled team. Ideal candidates have a strong background in training with PyTorch and experience in optimizing LLMs. This role offers competitive compensation ranging from $250K to $350K, along with equity and benefits. #J-18808-Ljbffr

A forward-thinking sleep technology company in San Francisco is seeking a research scientist to leverage AI and Machine Learning to transform health and fitness experiences through innovative technology. The ideal candidate has a PhD in a relevant field and practical experience in machine learning application, with a passion for health technologies. This role promises immediate responsibilities, collaboration with exceptional talent, and equitable compensation, while promoting a workplace that values innovation and excellence. #J-18808-Ljbffr

Substrate is addressing one of the most important technological problems facing the United States. At the intersection of advanced manufacturing and cutting‑edge physics, we are developing technologies that will reshape the semiconductor industry and strengthen America's technological leadership. We are a team of world‑class scientists, engineers, and technical experts building technology for the United States. Summary As a Research Scientist working with AI, you will accelerate and augment R&D workflows by applying machine learning to scientific simulations and modeling while simultaneously building internal AI capabilities across the organization. This role sits at the intersection of cutting‑edge physics and artificial intelligence, and you'll work hands‑on to develop AI‑augmented tools that enable breakthrough research, and build the infrastructure and expertise that empowers our technical teams to leverage AI in their own work. Whether you are a physicist who has embraced machine learning or an AI expert with deep scientific domain knowledge, you will play a pivotal role in defining how we utilize AI to accelerate our own internal R&D. Responsibilities Integrate machine learning techniques to accelerate scientific simulations, modeling, and computational workflows Develop AI‑augmented tools for materials science, device physics, or accelerator physics applications Build internal AI infrastructure and capabilities to enable research teams across the organization Train and mentor scientists and engineers on integrating AI/ML into their research workflows Implement surrogate models, physics‑informed neural networks, or generative approaches for scientific problems Develop data pipelines and frameworks for scientific machine learning across distributed teams Collaborate with computational physicists and experimentalists to identify high‑impact AI applications Set AI best practices and establish standards for ML‑augmented R&D Required Qualifications 5+ years professional or academic research experience in physical sciences, engineering, or related field 2‑3+ years hands‑on experience applying machine learning to scientific or technical problems Strong programming skills in Python and ML frameworks (PyTorch, TensorFlow, JAX, or similar) Deep understanding of scientific computing, numerical methods, and computational modeling Proven ability to translate scientific problems into machine learning approaches Experience building tools, infrastructure, or capabilities used by technical teams Preferred Qualifications PhD in Physics, Materials Science, Computer Science, Applied Mathematics, or related field Publications applying ML to scientific computing, simulation, or experimental data analysis Experience with physics‑informed machine learning or scientific foundation models Background in accelerator physics, semiconductor devices, materials modeling, or related domains Track record of enabling technical teams through tool development or mentorship Salary Range $150,000-$400,000 USD Substrate is an equal opportunity employer. It provides equal employment opportunity to all applicants without regard to race, color, religion, national origin, disability, medical condition, marital status, sex, gender, age, military or veteran status, or any other characteristic protected by applicable federal, state, or local laws. Substrate will provide reasonable accommodations to applicants with disabilities. If you need an accommodation during the hiring process, please let your recruiter know. Applicants must be legally authorized to work in the United States. This position is not eligible for visa sponsorship. #J-18808-Ljbffr

A leading AI science company in San Francisco is seeking an Applied Scientist to tackle complex marketing measurement challenges. The role involves designing innovative algorithms and collaborating with clients to ensure accuracy at scale. Ideal candidates will have at least 5 years of experience in research code and a strong mathematical background, including causal inference. The compensation ranges from $235K to $258K, with equity offers and the chance to work with cutting-edge technology in a dynamic startup environment. #J-18808-Ljbffr

A generative media company in San Francisco seeks a Research Scientist to lead innovation in video generation. Responsibilities include developing model compression techniques and optimizing for efficiency. The ideal candidate has a PhD or relevant industry experience in Machine Learning, particularly with diffusion models. The role offers competitive compensation, benefits, and an inspiring startup culture. #J-18808-Ljbffr

About P-1 AI: We are building an engineering AGI. We founded P-1 AI with the conviction that the greatest impact of artificial intelligence will be on the built world-helping mankind conquer nature and bend it to our will. Our first product is Archie, an AI engineer capable of quantitative and spatial reasoning over physical product domains that performs at the level of an entry‑level design engineer. We aim to put an Archie on every engineering team at every industrial company on earth. Our founding team includes the top minds in deep learning, model‑based engineering, and industries that are our customers. We just closed a $23 million seed round led by Radical Ventures that includes a number of other AI and industrial luminaries (from OpenAI, DeepMind, etc.). About the Role: We're seeking an exceptional AI Research Scientist to help us push the boundaries of AI applied to the physical world. This role blends cutting‑edge AI research with hands‑on engineering, and is ideal for someone who thrives at the intersection of ideas and implementation. You'll be leading projects that develop agentic AI systems designed to solve real‑world mechanical, electrical, and aerospace engineering problems-systems that think, remember, act, and adapt. This is not a “pure research” position: we're looking for a hacker‑scientist hybrid-someone who's published in top venues but is not afraid of any layer in the tech stack. What You'll Do: Design and implement agentic systems that autonomously solve physical engineering tasks. Develop advanced memory, retrieval, and planning architectures for LLM‑based agents. Apply (or invent) reinforcement learning strategies for reasoning, planning, and online adaptation. Contribute to both research strategy and technical implementation-this is a hands‑on role. Collaborate with a small, elite team of researchers and engineers across domains. Stay on the edge of what's possible and bring promising ideas into reality. About you: Have experience at the frontier of AI research (e.g., LLMs, RL, memory systems, agent architectures). Are passionate about applied problems-especially in mechanical, aerospace, or electrical engineering domains. Are fluent in Python and major ML/AI frameworks (PyTorch, JAX, etc.). Thrive in fast‑moving environments and feel comfortable working towards underspecified goals. Have a “whatever it takes” mindset: you're the kind of person makes things work. Can go from whiteboard to working prototype without waiting for someone else to “engineer” it. Preferred Qualifications: PhD in Computer Science, Robotics, Engineering, Math, or a related technical field (or equivalent experience). Relevant publications in top‑tier venues of your field. Experience with physical engineering domains a plus. Familiarity with agent tool‑use, retrieval‑augmented generation, or long‑term memory systems. Deep knowledge of reinforcement learning algorithms and practical challenges. Interview process: Initial screening - Head of Talent (30 mins) Hiring manager interview - Head of AI (45 mins) Technical Interview - AI Chief Scientist (45 mins) Culture fit / Q&A (maybe in person) - with co‑founder & CEO (45 mins) #J-18808-Ljbffr