Senior Robotics / Autonomy Engineer – AI Sensor & Edge Systems

S3StaffingusaSan Francisco, CA
Onsite

About The Position

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.

Requirements

  • 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

Nice To Haves

  • 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

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

Benefits

  • 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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