Applied AI Engineer - Flywheel Automation & Continuous Learning Job at Kodiak Robotics (Mountain View)

Job Description

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.

Job Responsibility

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

Requirements

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

Nice to have

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
Comfort working in cross-functional teams with research scientists, infra engineers, and robotics experts
A mindset of ownership, experimentation, and systematic improvement

What we offer

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 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.)

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Applied AI Engineer - Flywheel Automation & Continuous Learning

Job Description

Job Responsibility

Requirements

Nice to have

What we offer

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Applied AI Engineer - Flywheel Automation & Continuous Learning

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