Software Integration Engineer, Autonomy

42dot • San Francisco, CA

17h • $150,000 - $205,000

About The Position

42dot is hiring a Software Integration Engineer to bring autonomy machine-learning models from research and offline evaluation into reliable, real-time operation on vehicle hardware. The role works at the boundary between the ML autonomy team’s models and the on-board system, integrating and optimizing models for the vehicle’s compute and runtime stack.

This is a founding-team opportunity. The on-board integration architecture is being defined without a legacy deployment stack, giving an early hire the opportunity to help shape the model deployment pipeline, inference optimization strategy and tooling, and interface standards between ML models and on-board software.

Requirements

• Demonstrated grit and ownership, with the ability to work through ambiguous technical problems to a working solution.
• Bachelor's or Master's degree in Computer Science, Electrical Engineering, Robotics, or a related field.
• 2–4 years of experience in systems or software engineering, with some exposure to ML inference, embedded systems, or real-time software.
• Experience contributing to the deployment or optimization of ML models in production.
• Solid C++ and Python skills, with an interest in working across the ML and systems boundary.

Nice To Haves

• Background in autonomous driving or robotics on-board software.
• Exposure to inference optimization frameworks such as TensorRT and ONNX Runtime, and to automotive SoCs.
• Exposure to real-time operating systems and safety-critical software practices.
• Experience contributing to CI/CD and automated validation pipelines.

Responsibilities

• Integrate ML models, including end-to-end driving, VLA, and world models, onto the on-board compute and runtime stack.
• Optimize model inference for latency, throughput, memory, and power on embedded automotive hardware.
• Build and maintain the model deployment pipeline, including conversion, validation, and rollout.
• Stabilize on-vehicle ML execution by diagnosing runtime failures, timing violations, and resource contention.
• Define interface contracts between ML models and the rest of the on-board software stack.
• Work closely with the ML autonomy team to connect deployment constraints with model design.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level