Design and train innovative large-scale models—including generative, imitation, and reinforcement learning—to improve the planning and reasoning capabilities of our driving systems.
Build, pre-train, and fine-tune LLM/VLM/VLA systems for deployment in real-world autonomous driving and robotics applications.
Explore novel data generation and collection strategies to improve diversity and quality of training datasets.
Collaborate with cross-functional teams to deploy AI models in production environments, ensuring performance, safety, and reliability standards are met.
Integrate machine learning models directly with vehicle firmware to deliver production-quality, safety-critical software.

Requirements

Hands-on experience building LLMs, VLMs, or VLAs from scratch or a proven track record as a top-tier coder passionate about autonomous systems.
Deep understanding of modern deep learning architectures and optimization techniques.
Proven record of deploying production-grade ML models for self-driving, robotics, or related fields at scale.
Strong programming skills in Python and proficiency with major deep learning frameworks.
Familiarity with C++ for model deployment and integration in safety-critical systems.
Master's degree or PhD (or equivalent experience).
8+ years of work experience in AV or related field.

Benefits

equity
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Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

LLMVLMVLAdeep learning architecturesoptimization techniquesPythonC++machine learning modelsdata generationtraining datasets

Soft Skills

collaborationproblem-solvingcommunicationinnovationattention to detail

Certifications

Master's degreePhD