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: USC, 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
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