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

Build and maintain the software engineering backbone of Genesis-World, an open-source multi-physics robotics simulation platform. Responsibilities include scalable testing for JIT-compiled systems, deterministic simulation replay, autodiff integration, scene editing, plugin architecture, actuation, local time stepping, event systems, serialization, telemetry, performance improvements, and legacy refactoring. The role also involves code reviews, issue triage, contributor support, and collaboration with compiler and physics teams across GPU and operating-system platforms.

Develop and ship production-ready physics simulation capabilities for Genesis-World, improving speed, fidelity, completeness, versatility, and scalability. Implement and optimize multi-physics algorithms, constraint solvers, adaptive timesteps, contact resolution, and heterogeneous solver coupling using Python and GPU/CPU backends. Validate methods through analytical solutions, cross-engine comparisons, and real-world data, while delivering tested, documented code for robotics learning pipelines.

Own Quadrants, an open-source Python compiler for performance-critical numerical computing and physics simulation. Lead its API, intermediate representation, lowering, compiler backends, optimization passes, numerical primitives, benchmarks, roadmap, and user adoption. Optimize GPU and CPU execution, maintain system coherence, track regressions, improve usability, and collaborate with Genesis users. The role combines compiler research, GPU engineering, API design, open-source product ownership, and future leadership of a small team.

Design, build, and scale security infrastructure across systems, networks, endpoints, and products. Own security architecture, detection/response, vulnerability and risk management, and security policies. Integrate security into the SDLC, partner with engineering, run incident response and monitoring, lead security education, and temporarily own IT infrastructure.

Build and optimize low-latency on-device and distributed GPU inference pipelines. Implement and tune low-level CUDA/Triton kernels, optimize for throughput and latency, and develop monitoring/debugging tools to ensure reliable, deterministic inference in robotics and cluster environments.

Own customer-facing go-to-market for robotics: identify design partners, run pilots to production, close technical commercial deals, and feed customer insights into product and engineering. Lead deployments, build playbooks, define pricing, and align partnerships to scale early-adopter engagements.

Hands-on field engineer who keeps robot data collection running: stand up and maintain stations, triage and resolve hardware/software/data issues on customer and partner sites, bring up new tasks and sites, and feed operational insights back to model, platform, and hardware teams.

Lead development of a high-performance robotics simulation platform and its compiler stack. Design compute infrastructure and data flow for physics simulation and model training, optimize GPU codegen and JIT compilation, improve differentiable programming support, and contribute to open-source ML compiler projects while collaborating with simulation and robotics engineers.

Own and scale end-to-end physical-data collection operations: scheduling, operator utilization, station uptime, throughput and quality. Recruit and develop operators and shift leads, standardize playbooks and SOPs across sites, partner with Quality and Annotation teams, stand up partner-site deployments, and build reporting that tracks collected hours, quality, and cost.

Design, build, and optimize distributed PyTorch training systems for multi-node GPU clusters. Profile and eliminate performance bottlenecks across data pipelines to GPU kernels, implement low-level CUDA/cuDNN/Triton kernels, tune CPU/GPU/memory/network utilization, and develop monitoring and debugging tools for large-scale training runs.