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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.
We are looking for a highly skilled Infrastructure Engineer to join the Perception team and build world-class deep learning training, evaluation, and deployment infrastructure for the development of the perception and prediction modules of our self-driving semi trucks.
In this role, you will:
- Be responsible for designing and implementing Kodiak’s infrastructure for training, evaluating, and deploying deep learning models in efficient Python and C++ code
- Develop and optimize push-button training pipelines in distributed environments
- Develop metrics to measure model, perception system, and overall self-driving performance
- Build systems to track metrics over time and enable comparison of experiments
- Develop systems for finding failure cases, visualizing predictions, and pinpointing areas for improvement
- Create systems to track, segment, and select data to be used in training or reinforcement, including synthetic and machine-generated labels
- Measure and optimize deep learning network and detection runtime performance using state of the art techniques
- Enable continuous integration and deployment of model improvements
- Collaborate closely with other experts in sensors, perception, prediction, planning, and systems engineering teams
Preferred Qualifications:
- Strong technical background. BS, MS or PhD with academic or industry experience in designing and implementing deep learning, ML, or data analytics infrastructure. Skilled in Python/C++ and strong problem-solving skills. You ideally also have a strong knowledge of data processing pipelines for training ML models in the cloud.
- Very strong software engineering skills and experience building high-reliability applications used by other engineers. You write well-designed, highly maintainable code. Code is your craft.
- Solid understanding of metrics, data analysis, and scientific evaluation. Bonus points for understanding the specific subtleties in measuring and evaluating the performance of a robot and its submodules.
- A team player. You take ownership and work with the team to deliver exceptional results. You are interested in the performance of the entire system across engineering disciplines.
- Ability to build and iterate quickly. You enjoy working fast and smart, and you are comfortable in defining and building lasting infrastructure components from scratch.
- Hands on. You are experienced working with production machine learning pipelines, from dataset collection and labeling to training and validation.
- Great communicator. You have experience writing clear, concise, and detailed documentation. You can listen and understand what would enable your model development colleagues to measure and refine ML models more quickly and what is necessary to improve the performance of the Kodiak Driver.
What we can 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
- 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, One Medical, Gympass, Spring Health
- Fidelity 401(k)
- Commuter, FSA, Dependent Care FSA, HSA
- Various incentive programs (referral bonuses, patent bonuses, etc.)
The annual salary range for this full-time position is $150,000-$230,000 + bonus + equity + benefits. Actual salary ranges will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Please note that the compensation details listed above are base salary only and do not include bonus, equity, or benefits.
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.
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