Applied AI Engineer - Flywheel Automation & Continuous Learning

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 a world-class Applied AI Engineer to design and build the AI Flywheel - the closed-loop system that powers continuous learning across our fleet of autonomous trucks.

In this role, you will own the architecture and automation of a complete data-to-model flywheel: from mining hard edge cases, to orchestrating distributed training pipelines, to deploying models across our large-scale AI infrastructure. Your work will ensure that our models improve rapidly and continuously with every mile driven.

In this role, you will:

• Design and implement the end-to-end AI Flywheel, platforms for training, validation, deployment, and building a robust automated system.
• Build and maintain multi-node distributed training pipelines using tools like PyTorch DDP, Horovod, or Ray.
• Develop smart data mining and active learning strategies to prioritize valuable training data from petabyte-scale logs.
• Automate model evaluation and selection pipelines to support rapid iteration and closed-loop deployment.
• Build infrastructure for seamless model image packaging, validation, and rollout across Kodiak’s autonomous fleet and AI platform.
• Ensure that the flywheel is reliable, reproducible, and scalable, capable of learning from millions of real-world miles.

What you'll bring:

• Bachelor’s, Master’s, or PhD in Computer Science, Machine Learning, Robotics, or a related field.
• 3+ years of experience building production-grade ML infrastructure or model pipelines.
• Deep proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow).
• Experience with distributed training and pipeline orchestration (e.g., Airflow, Kubeflow, Dagster).
• Strong engineering fundamentals, debugging skills, and ability to scale systems.
• Passion for turning real-world data into self-improving AI systems.

Ideal candidate will also bring:

• Experience in autonomous vehicles, robotics, or other sensor-rich real-world ML systems.
• Prior work with self-supervised learning, active learning, or large-scale data curation.
• Familiarity with containerization (Docker), model packaging, and deployment workflows.