Do you enjoy applying machine learning to complex, real-world problems in autonomous vehicle testing? The Simulation Scenario Authoring team owns the formats and tools used to create synthetic simulation scenarios. We are looking for a hands-on ML Engineer to integrate, implement, and optimize our next-generation AV scenario generation workflow. This ranges from extending our AI assistant to advancing toward full scenario creation automation from 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:
Integrate and validate LLMs/VLMs and implement other models for complex scenario generation workflows, leveraging techniques like advanced prompting, agentic tool use, and more. Contribute to tooling for AI-based scenario understanding and validation.Collect data and design metrics to drive business intelligence, product iteration, and model fine-tuning.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 field2+ years of industry experience in Machine LearningSolid understanding of LLM or NLP conceptsProficiency in Python and ML libraries (PyTorch, NumPy) demonstrated through professional or research projectsBonus Qualifications
Practical experience in dataset creation for fine-tuning, system integration of ML models into production, or optimization techniques for low-latency inference systemsFamiliarity with autonomous vehicles, robotics, and/or complex simulation environmentsHands-on experience in areas like program synthesis, diffusion models, and/or formal methods/V&VRelevant publications in conferences (e.g., CVPR, ICCV, RSS, and/or ICRA)Additional
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