Responsibilities
- Own the technical vision and roadmap for Learned Vehicle Intent (LVI), aligning model development with product and safety goals
- Lead and scale 2–3 teams of ML engineers, driving execution across model development, training, and deployment
- Set and uphold a high bar for technical excellence in reinforcement learning, imitation learning, and behavior modeling
- Guide architecture decisions for policy learning systems, including behavior cloning, RL, and multi-agent interaction models
- Drive development of scalable training pipelines, data infrastructure, and experimentation frameworks
- Partner cross-functionally with perception, prediction, analytical planning, simulation, and safety teams to integrate learned behaviors into the autonomy stack
- Ensure robust validation and evaluation of behavior models, including failure analysis and performance tracking across simulation and real-world environments
- Establish best practices for data strategy, model iteration, and system-level integration
- Mentor senior and staff engineers, fostering growth, ownership, and technical leadership within the team
- Lead team planning, execution, and delivery, ensuring alignment with broader organizational priorities
Requirements
- 10+ years of experience in applied machine learning, autonomy, robotics, or related domains
- Proven experience operating at a Staff or higher technical level, with deep expertise in:
- Reinforcement learning
- Imitation learning / behavior cloning
- Multi-agent systems
- 5+ years of experience leading engineering teams, ideally across multiple teams or domains
- Demonstrated success defining and delivering technical roadmaps for complex ML systems
- Strong programming background in Python and modern ML frameworks (e.g., PyTorch, PufferDrive)
- Deep understanding of ML architectures and training systems, including scaling, data pipelines, and deployment
- Experience integrating ML models into production systems, ideally within autonomy or robotics stacks
- Strong cross-functional collaboration skills and ability to influence across teams
Nice to Have
- Experience in autonomous vehicles, trucking, or large-scale robotics systems
- Experience deploying RL or learned behavior models in production environments
- Familiarity with simulation environments, scenario generation, and validation frameworks
- Experience with Ray or distributed training infrastructure
- Background in system-level integration of ML models into planning or control systems
- PhD in Computer Science, Artificial Intelligence, or related field with a track record in learned behaviors or reinforcement learning
Benefits
- A competitive compensation package that includes a bonus component and stock options
- 100% paid medical, dental, and vision premiums for full-time employees
- 401K plan with a 6% employer match
- Flexibility in schedule and generous paid vacation (available immediately after start date)
- Company-wide holiday office closures
- AD+D and Life Insurance
Work Arrangement
Hybrid
Team
Team size: 2–3 teams. Structure: multiple teams responsible for developing and scaling learned behavior models
Additional Information
- Dependent on the position offered, sign-on payments, relocation, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits.
