Do you enjoy applying machine learning to complex, real-world problems in autonomous vehicle testing? The Simulation Scenario Generation team is looking for a ML Engineer to enable next-generation scalable AV scenario creation workflows. This ranges from generating large-scale traffic simulations to extending our agentic AI system to assist in synthetic scenario creation from a natural language test specification. This role offers a unique chance to deliver immediate user impact while contributing to long-term AI-driven safety validation.
In this role, you will:
Contribute to tooling for AI-based scenario understanding and validation.
Synthesize realistic AV simulation scenarios with dynamic (e.g., traffic) and static features.
Develop, integrate, and validate ML models (including custom models and off-the-shelf LLMs/VLMs) for complex scenario generation workflows, leveraging techniques like agentic tool use.
Collaborate directly with internal customers and partner teams to provide generative AI solutions for their test creation workflows.
Directly contribute to the safety and reliability of Zoox's autonomous software.
Qualifications
MS or PhD in Computer Science, Machine Learning, or related field
5+ years of industry experience in Machine Learning
Proficiency in Python and ML libraries (PyTorch, JAX, NumPy, etc.) demonstrated through professional or research projects
Demonstrated experience in transformer and diffusion architectures
Practical experience in dataset creation for fine-tuning
Bonus Qualifications
Familiarity with autonomous vehicles, robotics, and/or complex simulation environments
Hands-on experience in areas like program synthesis and/or formal methods/V&V
Relevant publications in conferences (e.g., CVPR, ICCV, RSS, and/or ICRA)
