AI Chief Engineering Lead Full Time

What You’ll Do

• Develop Advanced Agentic Software – Design and implement novel agent-based software systems to improve sensor perception, prediction, and decision-making for autonomous vehicles
• Apply Agentic Reasoning – Design and implement integrated agents and AI models to solve for end-user autonomous systems workflows.
• End-to-End System Integration – Collaborate with cross-functional teams to integrate research prototypes into robust, production-ready systems including simulation environments and real-world platforms.
• Research & Experimentation – Conduct research into reinforcement learning strategies and deep architectures, iterate on experimental designs, and evaluate performance using rigorous quantitative metrics.
• Data-Driven Innovation – Utilize real-world and synthetic data to enhance model robustness and generalization, leveraging scalable training pipelines on distributed systems.

Required Qualifications

• Sophisticated knowledge of LLM’s with an understanding of how they work and how they’re applied
• Solid experience with reinforcement learning methods and their application to autonomous systems.
• Proven experience of shipping products end to end
• Experience with simulation or real-world validation for autonomous vehicles is highly desirable.
• A degree in Computer Science, Robotics, Machine Learning, or a related field, or equivalent practical experience
• Eligible to obtain and maintain an active U.S. Top Secret security clearance
• Travel up to 40+% of time to build, test, and deploy capabilities in the real world

Preferred Qualifications

• PhD or Master’s degree in Computer Science, Robotics, Machine Learning, or a related field, or equivalent practical experience
• Novel application track record and experience including first author publications, participation in peer reviewed conferences, contribution to open source projects, and demonstrated contribution to the ML and AI community.
• Proven experience in deep learning research and development, particularly in generative AI. This includes diffusion models and autoregressive generative models.
• Experience in multi-modal sensor data processing (e.g., cameras, LiDAR, radar).
• Familiarity with ML Ops best practices, including model versioning and reproducible research pipelines.
• Strong programming skills in Python and familiarity with C/C++ is a plus.
• General software engineering experience solving motion planning or related robotics problems.