Staff Machine Learning Engineer - Deployment

Kodiak is seeking Staff Machine Learning Engineers, focusing on model deployment to help build the intelligence that powers the Kodiak Driver. Our ML teams work across perception, prediction, planning, and AI infrastructure to turn real-world driving data into models that enable safe and scalable autonomous trucking. In this role, you will design and deploy machine learning systems that improve our vehicles’ ability to understand the world, predict the behavior of other road users, and make safe driving decisions. You will collaborate closely with robotics, autonomy, and infrastructure teams to continuously improve the performance of our autonomy stack using large-scale data from our growing fleet.

Responsibilities

Help design, train, with a focus towards deploying onboard machine learning models that improve the performance/latency of the Kodiak autonomy stack which includes quantization, pruning, converting to ONNX/TensorRT, custom GPU kernels and profiling; Help identify and achieve parity between onboard and deployed offboard models using validation on HIL; Collaborate with cross-functional teams including perception, planning, simulation, and autonomy infrastructure to integrate ML models into production systems; Analyze real-world driving logs to identify edge cases and improve model robustness and safety; Use profiling tooling to additionally help improve the train time for models by identifying and removing bottlenecks; Contribute to the development of scalable AI infrastructure that supports continuous learning and deployment across Kodiak’s fleet

Requirements

MS or PhD in Computer Science, Robotics, Machine Learning, or a related technical field, or equivalent practical experience; 6+ years experience deploying machine learning or deep learning systems in production environments; Proficiency in Python, C++ and modern ML frameworks such as PyTorch or TensorFlow; Experience working with large-scale distributed training systems; Solid understanding of optimization, and machine learning fundamentals; Strong software engineering fundamentals including testing, debugging, and system design; Ability to work collaboratively across teams to solve complex autonomy and robotics challenges

Benefits

Competitive compensation package including equity and annual bonuses; Excellent Medical, Dental, and Vision plans through Kaiser Permanente, Cigna, and MetLife (including a medical plan with infertility benefits); MetLife Legal Services, Identity & Fraud Protection, Hospital Indemnity Insurance, Accident Insurance, & Critical Illness Insurance; Flexible PTO, 10 paid holidays, and generous parental leave policies; Office is centrally located in Mountain View, CA; Office perks: dog-friendly, free catered lunch, a fully stocked kitchen, and free EV charging; Long Term Disability, Short Term Disability, Life Insurance; Wellbeing Benefits - Headspace through Cigna, Calm through Kaiser, One Medical, Gympass, Spring Health through Cigna, Rula (mental health navigation); Fidelity 401(k); Commuter, FSA, Dependent Care FSA, HSA; Various incentive programs (referral bonuses, patent bonuses, etc.)