reinforcement-learning jobs in San Diego

As an AI and Data Science Engineer III, you will translate business goals into machine learning use cases and develop predictive models for marketing and advertising applications. You'll perform exploratory data analysis, apply clustering and feature transformation techniques, build proofs of concept, and validate solutions.…See more The role requires 5+ years of industry experience applying data science or machine learning methods, with a strong foundation in translating business requirements into model design approaches and collaborating across analytics and data engineering teams.

As an Autonomy Engineer focused on VLA (Vision-Language-Action) pre-training, you will develop and train deep learning policies for humanoid robots, owning the full pipeline from data collection and curation to model deployment. You'll pre-train base models on diverse multi-embodiment trajectory corpora, fine-tune policies for specific tasks, work with MLOps teams to scale distributed training, and build continuous pipelines that ingest synthetic data and teleop logs.…See more This is primarily a deep learning role requiring 3+ years of experience shipping neural network models, hands-on expertise with LLMs, VLMs, or generative models, and strong proficiency in Python and PyTorch or JAX.

Lead the scientific vision and execution of a generative AI platform for protein foundation models, starting with antibody and VHH design. You will build and manage a team of AI researchers and ML infrastructure engineers, define pretraining and post-training strategies across self-supervised learning, reinforcement learning, and transfer learning, and design confidence heads with out-of-distribution detection.…See more This is a founding role requiring deep expertise in structure-based generative modeling, multi-node GPU training, and the ability to shape cutting-edge computational approaches for therapeutic molecule discovery.

Senior AI/ML Engineer responsible for shipping end-to-end production ML systems that enable autonomous medical coding in a SaaS platform. Will design robust ML architectures with low-latency inference, build reliable data and model pipelines using modern MLOps practices (CI/CD, model registries, experiment tracking), establish evaluation strategies with statistical rigor, and implement model observability and governance including drift detection and bias assessments.…See more Requires 5+ years of industry experience building and operating ML systems in production, 3+ years with C# or Python, 3+ years on Azure, and 1+ years of LLMOps expertise including prompt engineering and RAG.

Build and ship production ML systems end-to-end for autonomous medical coding in a SaaS platform, from problem framing through deployment and monitoring. Own the full lifecycle—data discovery, feature engineering, training, evaluation, and iteration—working with LLMs, deep learning, and modern MLOps tooling to handle millions of medical charts daily.…See more Design robust, low-latency architectures, establish rigorous evaluation strategies, and implement model observability, drift detection, and bias assessments. Collaborate across product, data, and platform teams to translate business goals into measurable ML outcomes.

Senior Robotics Software Engineer responsible for building motion and trajectory planning systems for robotic manipulators, including kinematics, inverse kinematics, and collision-free path planning. You will develop the driver layer connecting to robot hardware, implement learning-based methods and simulation (reinforcement learning, sim-to-real), and contribute to the full robotics software stack (behaviors, motion planning, world modeling, device control).…See more You'll write validation suites, collaborate with hardware teams, and mentor engineers while maintaining strong coding and safety practices in a fast-paced startup environment.

Staff Engineer responsible for designing and building scalable test automation frameworks and CI/CD infrastructure for machine-learning systems at scale. The role requires expertise validating ML quality across the full lifecycle (data, training, evaluation, packaging, deployment, monitoring), GPU-accelerated workloads in Kubernetes, and integrated hardware-software systems.…See more You will develop automated evaluation suites, performance baselines, release thresholds, and observability solutions while mentoring teams on ML testing best practices and dependency/reproducibility standards.

Staff Engineer responsible for designing and implementing scalable test automation frameworks and infrastructure for machine learning systems across multiple engineering teams. The role requires expertise in validating ML pipelines across data, training, evaluation, deployment, and monitoring; testing GPU-accelerated workloads in Kubernetes; qualifying integrated hardware and software systems; and developing comprehensive integration and regression strategies.…See more Deep experience with Python automation, distributed systems testing, performance profiling, and observability is essential, along with strong system-design skills for complex, multi-tenant environments.

Research Programmer III will develop machine learning and computational methods to understand behavior and neural activity in the Kennedy Lab at Scripps Research. The role involves building and training deep neural networks for supervised, unsupervised, and reinforcement learning problems, applying computer vision for high-throughput animal phenotyping, and contributing to scientific papers.…See more Candidates must have 4+ years of software development experience in Python with demonstrated expertise in deep learning, version control (git/GitHub), and software engineering principles. The position offers opportunities to lead independent research projects on datasets from collaborating institutions and is ideal for those building toward graduate studies in computational neuroscience or machine learning.

Develop, optimize, and validate AI/ML solutions that leverage Qualcomm's AI inference accelerators for data center and hybrid applications. Design and implement GenAI and LLM applications, perform model benchmarking, optimize deployment strategies, and drive system-level architecture decisions.…See more Apply deep expertise in ML frameworks, system performance profiling, and parallel computing to ensure best-in-class inference performance, power efficiency, and scalability across heterogeneous hardware.

Design and implement robotics software for multi-robot coordination, motion planning, and trajectory execution. Own the driver layer connecting to robot hardware, develop kinematics and collision-free planning algorithms, and advance the stack with learning-based methods including reinforcement learning and sim-to-real training.…See more Mentor engineers, establish technical direction for the robotics software stack, and ensure motion planning and control systems are reliable, safe, and production-ready on real hardware.

This Staff AI Engineer role focuses on architecting and implementing secure, scalable frameworks for autonomous AI agents at enterprise scale. The engineer will lead the technical roadmap for agent management—from provisioning and orchestration to compliance and retirement—while ensuring AI agents are secure by design with built-in visibility and guardrails.…See more The role requires 8+ years of software architecture and backend systems experience, deep knowledge of autonomous agent frameworks (LangChain, AutoGen, CrewAI), and expertise in cloud infrastructure (AWS, Azure, GCP) and Kubernetes. You'll collaborate cross-functionally with researchers, platform engineers, and product teams to bring agentic AI capabilities to production while establishing best practices for agent safety, observability, and ethical deployment.

Qualcomm seeks a Machine Learning Engineer to design, develop, and deploy generative AI solutions focused on Large Language Models, Retrieval-Augmented Generation (RAG), and intelligent agent systems. The role involves implementing RAG-based solutions, optimizing LLM fine-tuning strategies, building evaluation frameworks, and maintaining agentic workflows for autonomous AI systems.…See more The candidate must have strong Python programming skills, practical LLM deployment experience, and familiarity with ML frameworks like PyTorch and TensorFlow, alongside knowledge of vector databases, cloud platforms (AWS, GCP, Azure), and modern MLOps infrastructure.

Engineering Lead responsible for hands-on ML research, model development, and production AI system deployment in a live trading environment. You will work closely with quantitative research leadership to shape and execute the research agenda, design and train models, lead day-to-day technical execution across the engineering team, and mentor engineers to raise overall technical standards.…See more The role requires 4–6 years of applied ML/AI experience with 1–2 years of technical leadership, strong proficiency in Python and PyTorch, and a track record of shipping production ML systems where model performance directly impacts real-world outcomes.

Join ByteDance's multimedia research lab as a Research Scientist to analyze, model, and optimize large-scale streaming systems serving billions of users. You will design novel algorithms and architectures for next-generation immersive media formats (360-degree video, volumetric video, light field), develop QoE evaluation metrics and methodologies, and conduct research at the intersection of multimedia, distributed systems, and networking.…See more Success requires a PhD in Computer Science, Electrical Engineering, or related discipline; strong fundamentals in algorithms, data structures, systems, and networking; and a proven publication track record in multimedia, networking, or systems research.

Engineering Lead responsible for designing, developing, and deploying production ML models for live trading environments while leading day-to-day technical execution across the engineering team. You will work with leadership to shape the research agenda, mentor engineers, drive adoption of AI tools to accelerate engineering workflows, and build/improve core ML infrastructure including data pipelines, training workflows, and inference systems.…See more The role requires 4–6 years of applied ML/AI experience with 1–2 years leading technical projects or teams, strong proficiency in Python and PyTorch, and a track record of shipping ML systems where model performance directly impacts business outcomes.

Develop prototype AI/ML solutions for ASIC design automation at NXP, exploring state-of-the-art techniques like GenAI, reinforcement learning, graph neural networks, and CNNs to improve VLSI CAD workflows. The role involves collaborating with architecture, IP, and SoC design teams to evaluate and pilot automation applications on leading-edge semiconductor technologies.…See more Candidates must have 5+ years of AI/ML expertise, strong Python/C++ programming skills, and hands-on experience with Unix/Linux, Git, and software development tools. Understanding of VLSI design constraints, EDA tools, and physical design flows is highly desirable.

This Staff AI Engineer role focuses on architecting and implementing secure, scalable frameworks for autonomous AI agents at enterprise scale. You'll lead the technical roadmap for agent management—from discovery and provisioning through task orchestration—while ensuring safety, compliance, and governance by design.…See more Working cross-functionally with product, platform, and research teams, you'll build foundational infrastructure for multi-agent collaboration, memory management, and extensibility, driving adoption across the enterprise. The role requires 8+ years in software architecture, backend systems, and Kubernetes/AI infrastructure, with deep expertise in LLM frameworks (LangChain, AutoGen, CrewAI), cloud platforms (AWS, Azure, GCP), and identity/access control patterns.

Design and develop Qualcomm's in-house robotics simulation platform and AI infrastructure, integrating modern 3D engines, GPU-accelerated compute, and machine learning to enable robot policy training and validation. Build digital twins, synthetic data pipelines, and hardware-in-the-loop configurations that bridge simulation and real Qualcomm robotics hardware.…See more Partner with robotics researchers to iterate on simulation fidelity, sensor modeling, and continuous learning workflows that move algorithms from simulation to deployed robots. This role demands depth in robotics simulators (MuJoCo, Isaac Sim, Unreal/Unity), GPU programming (CUDA), reinforcement learning at scale, and AI infrastructure orchestration (Kubernetes, Argo Workflows).

Senior Staff Engineer responsible for designing and building an MLOps platform that supports distributed AI training, reinforcement learning, and foundation model development at scale. You will architect Kubernetes-native infrastructure for GPU workloads, design self-service AI development workflows, manage the data and model lifecycle, and lead platform distribution across cloud, on-premises, and air-gapped environments.…See more The role requires deep expertise in modern AI frameworks (PyTorch, Hugging Face Transformers), distributed systems, GPU scheduling, and cloud-native infrastructure, with a focus on enabling researchers and engineers to move from experimentation to production rapidly.

Lead ML research and production model development for a quantitative trading firm, working hands-on to design, train, and deploy ML systems that directly impact live trading environments. You will drive technical execution across the engineering team, mentor engineers to raise the bar, and translate ambiguous research problems into executable modeling work with measurable real-world impact.…See more The role requires 4–6 years of applied ML experience with 1–2 years of technical leadership, strong fluency in Python and PyTorch, and a track record of shipping production systems in high-stakes contexts.

Shield AI seeks a Senior Staff Engineer to design and deploy autonomous capabilities for maritime surface vehicles. The role spans algorithm development, real-time software engineering, system integration, and field testing—requiring deep expertise in robotics, motion planning, decision-making, and unmanned systems.…See more You will collaborate across perception, planning, simulation, and hardware teams to mature autonomy solutions from prototypes to operational deployment, with ~25% travel to integration events and customer sites.

Senior Software Engineer responsible for developing high-performance autonomy software modules for maritime unmanned systems, from algorithm prototyping through real-world deployment and testing. The role requires 7–10+ years of experience in robotics, control systems, motion planning, or autonomous vehicle development, with proficiency in C++ and Python.…See more Responsibilities include cross-functional collaboration with perception and planning teams, integration of autonomy capabilities onto surface vehicles, analysis of mission logs, and contribution to autonomy roadmaps through R&D and algorithm innovation.

Build and integrate agentic AI systems with physical laboratory automation platforms (Hamilton, Tecan, Opentrons) and LIMS infrastructure to autonomously design, run, and refine chemistry and biology experiments. Design multi-agent orchestration architectures with state management, error recovery, and tool integration; prototype agent planning strategies and memory systems; deploy containerized services on Kubernetes with GitOps and CI/CD.…See more Work hands-on with chemists, biologists, and automation engineers to transition prototypes into production lab operations that measurably accelerate the Design-Make-Test-Analyze cycle.

Principal Machine Learning Engineer at AppFolio to architect and lead mission-critical AI systems across the Realm-X property management platform. You'll design advanced AI agentic systems combining reasoning, planning, and execution; establish ML platform primitives for end-to-end production workflows; and drive the transition toward autonomous property management using LLMs, fine-tuning, and reinforcement learning.…See more Requires 10+ years building software systems with deep expertise in traditional ML, deep learning, generative AI, LLM post-training (SFT, RLHF, DPO, RL), and production ML at scale—plus a Master's or Ph.D. in Computer Science or related field.