AI Embedded Engineer IV

Responsibilities

• Deploy trained models to embedded Central Processing Units (CPUs) and Graphics Processing Units (GPUs) on vehicle compute, owning export, optimization, quantization, and latency budgets.
• Retrain and fine-tune existing models when field performance drifts, and validate the result on the vehicle rather than only on a benchmark.
• Build and maintain ROS nodes, topics, and interfaces running on the vehicle, and keep them stable under real workloads.
• Write the Python and C++ bridges that connect compute to robot control, sensor drivers, and the rest of the autonomy stack.
• Profile and tune runtime performance against real constraints, including compute headroom, memory, thermal limits, and power budgets.
• Bring up new sensors and compute hardware, including NVIDIA Jetson platforms, LiDAR units, and depth and vision systems, through provisioning, configuration, and calibration.
• Design and build sensor and data-collection rigs, covering sensor selection, mounting, wiring, power, networking, and onboard recording, then take them into the field.
• Debug hard cross-boundary problems spanning timing, synchronization, coordinate frames, machine motion, and compute limits.
• Fabricate and modify the mounts, brackets, and fixtures your hardware needs, and build and debug supporting wiring, harnesses, and small custom circuits.
• Characterize what you deliver honestly, including where it works, where it fails, and what conditions break it, so downstream teams know what they are inheriting.
• Document approaches, assumptions, results, and known limitations, and hand off work the production teams can build on with confidence.
• Provide technical guidance to other engineers on embedded deployment, sensor integration, and on-vehicle debugging.

Requirements

• Bachelor's degree in Robotics, Computer Science, Computer Engineering, Electrical Engineering, Mechanical Engineering, or a related technical field.
• Substantial experience developing embedded, robotics, or autonomous system software.
• Demonstrated experience independently taking complex embedded or AI integration work from concept to a working, evaluated system on real hardware.
• Advanced proficiency in C++ and Python.
• Experience deploying trained neural networks to embedded or production runtime environments, including model export and runtime optimization.
• Experience retraining or fine-tuning existing models and validating performance changes.
• Strong experience with Robot Operating System (ROS or ROS 2) or comparable robotics middleware on real vehicles or robots.
• Hands-on experience bringing up embedded compute platforms such as NVIDIA Jetson, including provisioning, drivers, and configuration.
• Experience integrating LiDAR, depth cameras, or other vision systems, including calibration and data synchronization.
• Working understanding of coordinate systems, geometric transformations, camera models, and sensor timing.
• Experience with Linux, version control, automated testing, and containerized development.
• Genuine willingness to work hands-on with hardware, including wiring sensors, assembling rigs, and debugging electrical and mechanical problems directly.
• Strong analytical and debugging skills, and experience explaining results and limitations clearly while providing technical guidance to other engineers.
• Willingness to travel to test sites as required.