Machine Learning Engineer - Deployment

Confirmed live in the last 24 hours

Kodiak Robotics

San Francisco Bay Area

On-site

Posted April 20, 2026

Job Description

Kodiak Robotics, Inc. was founded in 2018 and has become a leader in autonomous ground transportation committed to a safer and more efficient future for all. The company has developed an artificial intelligence (AI) powered technology stack purpose-built for commercial trucking and the public sector. The company delivers freight daily for its customers across the southern United States using its autonomous technology. In 2024, Kodiak became the first known company to publicly announce delivering a driverless semi-truck to a customer. Kodiak is also leveraging its commercial self-driving software to develop, test and deploy autonomous capabilities for the U.S. Department of Defense.

Kodiak is seeking Machine Learning Engineers focussing 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.

This is a high-impact opportunity to work on cutting-edge AI systems operating in the real world, where every mile driven improves our models and brings autonomous trucking closer to global deployment.

In this role, you will:

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.

What you’ll bring: