Applied AI Engineer, Kodiak Robotics

• 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

Desirable

• 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
• This is a high-impact, cross-functional role where you’ll interface with our perception, foundation model, and infrastructure teams to transform real-world driving data into smarter models and safer autonomy
• 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