Sr. Software Integration Engineer, Autonomy

Responsibilities

• Integrate ML models onto onboard automotive compute and runtime stacks.
• Optimize model inference for latency, throughput, memory, and power on resource-constrained embedded hardware.
• Build and maintain model deployment pipelines covering conversion, validation, and rollout.
• Diagnose runtime failures, timing violations, and resource contention in on-vehicle ML execution.
• Define interface contracts between ML models and onboard software.
• Collaborate with the ML autonomy team to connect deployment constraints with model design.

Requirements

• Bachelor’s, master’s, or PhD in computer science, electrical engineering, robotics, or a related field.
• 5+ years of systems or software engineering experience, including meaningful experience with ML inference, embedded systems, or real-time software.
• Track record of ownership and successfully moving ambiguous, difficult problems through deployment.
• Experience deploying and optimizing production ML models on resource-constrained hardware.
• Strong C++ and Python skills across the ML and systems boundary.
• Preferred: autonomous driving or robotics onboard-software experience.
• Preferred: experience with TensorRT, ONNX Runtime, automotive SoCs, real-time operating systems, safety-critical software practices, CI/CD, and automated validation pipelines.

Benefits

• Compensation is listed at $200,000–$300,000 per year.
• Founding-team opportunity to shape the model deployment pipeline, inference optimization strategy, and ML/onboard software interface standards.
• Equal opportunity employer with encouragement for applications from U.S. veterans and candidates eligible for employment preference.
• Qualified individuals with disabilities are encouraged to apply under the ADA.