Top Tech Jobs & Startup Jobs

Develop and optimize a learning-based robotic manipulation control stack; design and maintain low-latency teleoperation systems; train and deploy policies using reinforcement and imitation learning; diagnose control-pipeline latency and sim-to-real gaps; co-optimize simulation and real-world robot behavior; collaborate on building general-purpose Physical AI.

Design, build, and optimize distributed PyTorch training systems for multi-node GPU clusters. Profile and eliminate performance bottlenecks across data pipelines, kernels, and networking. Implement low-level CUDA/cuDNN/Triton kernels, tune hardware-software interactions, and develop monitoring and debugging tools for large-scale training runs to maximize utilization and reduce time-to-convergence.

Design, build, and operate large-scale distributed systems to improve reliability, scalability, and efficiency. Automate toil, perform capacity planning and performance analysis, prevent incidents, and provide cross-team technical leadership to balance system health with product velocity.

Own customer-facing go-to-market for robotics: identify design partners, drive pilots to production, manage complex technical sales, translate customer insights to product and engineering, define pricing and deployment playbooks, and build long-term partnerships in operational environments.

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.

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.