Top Tech Jobs & Startup Jobs in San Francisco, CA

Technical leader who architects and delivers end-to-end ML and computer vision pipelines to reconstruct and maintain map primitives from multi-modal vehicle sensor data, driving national-scale, production mapping systems and cross-functional integrations.

Lead design, development, and deployment of state estimation, localization, and sensor calibration systems for onboard autonomous vehicles. Architect scalable, robust real-time solutions, mentor engineers, drive technical roadmaps, and integrate capabilities across multidisciplinary teams to support safety-critical autonomous driving.

Build and optimize scalable analytics frameworks and tooling to accelerate autonomous vehicle development, testing, and deployment. Implement automation for test creation, data collection, and performance monitoring; create extensible C++/Python shared libraries; collaborate with simulation, data infra, and AV engineering to enable rapid model feedback and maintain high code quality.

Own and evolve end-to-end ML model compilation toolchain for AVs, designing compiler passes, improving build/runtime performance, collaborating with kernels/runtime/hardware teams, and building validation and debugging tooling to ensure correctness, performance, and reliability for on-vehicle inference.

Develop and evolve the model compilation toolchain to convert high-level ML models into optimized inference artifacts for vehicle accelerators. Architect compiler passes and analyses to improve build times, memory, and latency. Collaborate with kernels, runtime, and hardware teams, and build validation, regression detection, and diagnostics tooling to ensure correctness, performance, and reliability for on-vehicle deployment.

Design and implement high-performance IPC and middleware for AV/robotics (publish/subscribe, shared memory, serialization, discovery, QoS). Improve latency, throughput, and determinism via profiling and benchmarking; build developer APIs, tooling, observability, and production-grade reliability. Collaborate cross-functionally, apply production rigor, and mentor engineers.

Research, design, develop, and test machine learning and robotic solutions for autonomous vehicles. Own the full ML cycle: data collection, labeling, processing, model design, training, evaluation, and optimization. Develop novel deep learning architectures and sensor-fusion approaches combining camera, radar, LiDAR, and HD maps. Improve object detection, kinematics, intent, and short-term trajectory prediction. Support debugging of real-time perception issues on ride-hail fleets.

Design, implement, benchmark, and optimize CUDA-based GPU kernels and custom operators for on-vehicle ML inference. Build profiling and validation tooling, collaborate with compilers, runtime, and architecture teams, and deliver high-performance, production-grade libraries meeting strict latency, throughput, and reliability targets.

Design, develop, and deploy state estimation, localization, and sensor calibration systems for autonomous vehicles. Build validation, continuous monitoring, CI/regression tests, simulation scenarios, and prototyping/debugging tools. Collaborate with cross-functional teams and influence technical roadmaps to integrate localization and sensor alignment into AV systems.

Lead operational execution of GM's enterprise Privacy and AI governance programs: establish processes, run governance forums, build training and awareness, develop program metrics and executive reporting, manage documentation and content, operationalize AI impact assessment workflows and governance platform, and drive continuous improvement through automation and stakeholder coordination across global teams.