Senior Robotics / Autonomy Engineer AI Sensor & Edge Systems - 15-20+ Years of Experience || San Francisco, CA ||

Job Title: Senior Robotics / Autonomy Engineer AI Sensor & Edge Systems - 15-20+ Years of Experience Duration: 6+ Months (Contract) Location: San Francisco, CA Onsite required (No remote option) - Need only locals In-Person Technical Interview (San Francisco) 3 openings Visa Status: GC Focus on below : Background / Companies: Tesla (Autopilot, Dojo, Data Engine), Uber ATG/Aurora, Cruise, Waymo deployment teams (non-research), Zoox, Nuro, robotics startups, defense robotics, and industrial, agriculture, mining, or construction autonomy companies Core Profile: Hands-on autonomy builders who design, deploy, and iterate on real systems rather than research-only work Key Resume Signals: Built and deployed perception systems on real vehicles or machines; experience with data engines and fleet-scale data pipelines (not just model training); edge inference on real hardware Hardware & Stack Indicators: NVIDIA Jetson Orin/Xavier, ROS/ROS2, CAN bus, LiDAR, cameras, and multi-sensor integration Deployment Experience: Onsite testing, field deployments, hardware bring-up, debugging in real-world conditions, and built from scratch language Strong Keywords: Fleet, vehicles, telemetry, logs, edge AI, sensor fusion, low-latency pipelines Typical Titles: Autonomy Engineer, Perception Engineer, Robotics Software Engineer, Senior/Staff Autonomy Engineer, Robotics Engineer, Embedded AI Engineer, Edge AI Engineer Role Overview We are building next-generation sensor and edge-compute systems that enable heavy machinery to see, understand, and learn from the real world. This role is for a high-velocity implementer, not a researcher. You will design, build, deploy, and iterate on real-world AI systems mounted on large machines operating in harsh environments. If you thrive under tight timelines, imperfect data, brutal bandwidth constraints, and hands-on hardware integration, this role is for you. This initiative is part of a broader vision to collect large-scale machine data for reinforcement learning, imitation learning, and foundation model development, similar to how Tesla approaches autonomy. Key Responsibilities Design and deploy modular sensor stacks (LiDAR, cameras, CAN bus, edge compute) across multiple machine types Build low-latency AI pipelines under constrained bandwidth and compute environments Integrate edge AI systems with heavy machinery and autonomous platforms Enable efficient data capture, filtering, and offloading via 5G / Wi-Fi Identify critical data signals required for model training and continuously refine data pipelines Rapidly iterate on AI workflows to support reinforcement learning and imitation learning Work onsite to mount, test, debug, and harden systems in real-world conditions Deliver large amounts of functional output under aggressive timelines Required Technical Skills Strong hands-on experience in AI development for Robotics / Autonomy Proficient in C++ and/or Python Experience building sensor stacks including: LiDAR Cameras CAN BUS Edge compute systems Prior work with: Autonomous vehicles Robotics platforms UAV sensing systems DIY robots that operate in real environments Comfortable working with imperfect data, hardware constraints, and field deployments Preferred Skills Experience with transformer-based models and AI agents Familiarity using multiple AI agents simultaneously Exposure to reinforcement learning, imitation learning, or world models Background in edge AI optimization and bandwidth-constrained systems Previous experience at Uber, Tesla, or similar autonomy-driven companies Soft Skills & Mindset Self-starter / go-getter mentality Extremely autonomous and execution-focused Thrives in fast-paced, ambiguous environments Comfortable cutting corners when needed to deliver results Strong problem-solving instincts with a make it work attitude Energized by shipping in weeks, not publishing in months Why This Role Direct impact on real machines in the field Opportunity to shape large-scale AI data collection for foundation models Work alongside senior architects tackling complex autonomy challenges High ownership, high trust, and rapid execution Not research-oriented this is about building and deploying