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
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