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Design and implement AI Flywheel for continuous learning of autonomous trucks, manage distributed training pipelines, and ensure scalable AI infrastructure.
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.
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.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.
- 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
- Our office is centrally located in Mountain View, CA
- 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.)
The pay range listed below reflects the base salary in our SF/Silicon Valley location, across several internal levels. Actual starting pay will be based on job-related factors including: work location, experience, relevant training, education, skill level and performance during interview. Total compensation at Kodiak includes base pay, equity, bonus and a competitive benefits package
California Pay Range
$180,000—$240,000 USD
At Kodiak, we strive to build a diverse community working towards our common company goals in a safe and collaborative environment where harassment of any kind is strictly prohibited. Kodiak is committed to equal opportunity employment regardless of race, ethnicity, religion, gender identity, sexual orientation, age, disability, or veteran status, or any other basis protected by applicable law.
In alignment with its business operations, Kodiak adheres to all relevant statutes, regulations, and administrative prerequisites. Accordingly, roles that carry more sensitive requirements may be limited to candidates that can satisfy additional scrutiny and eligibility for such positions may hinge on verification of a candidate’s residence, U.S. person status, and/or citizenship status. Should the position require, and Kodiak determines that a candidate’s residence, U.S. person status, and/or citizenship status necessitate an export license, bar the candidate from the position, or otherwise fall under national security-related restrictions, Kodiak will consider the candidate for alternative positions unaffected by such restrictions, under terms and conditions set forth at Kodiak’s sole discretion, or, as an alternative, opt not to proceed with the candidate’s application. If applicable, Kodiak may provide visa sponsorship for eligible candidates.
Top Skills
Airflow
Dagster
Docker
Kubeflow
Python
PyTorch
TensorFlow
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