Applied AI Engineer - Flywheel Automation & Continuous Learning Jobs

Kodiak Robotics

Location:
United States, Mountain View

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

180000.00 - 240000.00 USD / Year

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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 biannual bonuses
Excellent Medical, Dental, and Vision plans through Kaiser Permanente, Anthem, and Guardian (including a medical plan with infertility benefits)
Flexible PTO 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, One Medical, Gympass, Spring Health
Fidelity 401(k)
Commuter, FSA, Dependent Care FSA, HSA
Various incentive programs (referral bonuses, patent bonuses, etc.)

Additional Information:

Job Posted:
December 09, 2025

Employment Type:

Fulltime

Work Type:

On-site work

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Applied AI Engineer - Flywheel Automation & Continuous Learning