Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Design, build, and maintain petabyte-scale batch and streaming data pipelines and core data infrastructure for robotics foundation model training. Own data models, storage, ingestion, transformation, and orchestration. Standardize pipelines across teleoperation and simulation datasets and collaborate with cross-functional team to support Physical AI development.

Develop, optimize, and deploy learning-based robotic manipulation and locomotion systems. Build low-latency teleoperation, train policies with reinforcement and imitation learning, diagnose control-pipeline bottlenecks, and minimize sim-to-real gaps through co-optimization of simulation and real robot behavior while collaborating with a cross-functional team.

Own and scale the pipeline that converts raw robot demonstrations (trajectory/video) into training-ready datasets. Design ontologies, run and automate annotation operations (internal and vendor), apply vision-language models for labeling and synthesis, track quality metrics, and close the loop from eval failures to targeted data collection to improve the model.

Lead the product roadmap for Physical AI, translating research into deployable robotics/autonomy products. Define readiness, success metrics, and reliability thresholds. Align research, engineering, and field teams, prioritize field validation over benchmarks, write clear specs and decision logs, and operate in ambiguous, fast-moving environments.

Lead and build the marketing function for a deep‑tech hardware startup. Translate research and engineering progress into accurate external communications, craft launch materials and narratives, run demos, lab visits, partner briefings, and technical press sessions, design conference presence, produce technical explainers, and support recruiting while hiring and operating hands‑on in a high‑uncertainty 0→1 environment.

Design and implement embedded control stacks, motion planning, and optimal-control algorithms for dynamic locomotion and manipulation. Build real-time state estimation fusing heterogeneous sensors, develop modular simulation-to-hardware software, and debug, tune, and validate controllers on physical robots.

Lead data-quality and process for robotic data collection: define acceptance standards and defect taxonomy, build QA and audit pipelines, own metrics (accept-rate, first-pass yield, rework, cost per accepted hour), close feedback loops with operators, standardize collection workflows, and translate model needs into measurable acceptance criteria.

Build a high-throughput, GPU-based simulation pipeline for robotics (primarily rigid-body). Implement actuators, sensors, and controllers; collaborate with robotics, policy training, and asset-generation teams to bridge sim-to-real and develop real-to-sim workflows for training robotics foundation models.