Top Tech Jobs & Startup Jobs

Design, prototype, and build robotic hardware systems; develop scalable data-collection platforms; improve simulation fidelity via system identification; maintain in-house hardware infrastructure; collaborate with engineering and simulation teams to deliver robust, scalable physical AI systems.

Build and run a combined sourcing and vendor-management function for data and compute. Source datasets and partners, negotiate contracts (MSAs/SOWs), forecast and secure compute capacity, run pilots and quality benchmarks, own data rights and compliance with Legal, and create scorecards, tooling, and metrics to manage spend, utilization, and supplier performance.

Create photoreal prop assets and digital twins end-to-end: modeling, high-poly sculpting, retopology, UVs, and PBR texturing. Convert and optimize photogrammetry scans to meet real-time poly/texture/memory budgets, collaborate with rendering and physics engineers, and maintain asset library and quality standards.

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.

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.

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.

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.

Design and implement low-latency, high-throughput inference pipelines for on-device and GPU-cluster deployment. Optimize kernels, batching, quantization, memory and scheduling. Build distributed serving systems, integrate low-level CUDA/Triton code with high-level frameworks, and develop monitoring/debugging tools to ensure reliability and determinism.

Own and implement end-to-end light field capture, generation, and reconstruction pipelines; build and optimize Gaussian splat training, compression, and real-time rendering; lead color science (camera characterization, color spaces, tone mapping, HDR, display calibration); productionize research papers into robust performant code; collaborate with capture, ML, and platform teams on pipeline architecture.

Operate robots onsite during data collection using teleoperation/VR equipment following SOPs. Execute repetitive tasks reliably, monitor and flag unsafe behavior, follow safety guidelines, provide session notes, and collaborate with operations and session leads in an R&D setting. Part-time, contractor role suitable for students.