<div><span style="font-size:11pt;">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.</span></div><br><br><h3>In this role, you will:</h3><ul><li>Integrate and validate LLMs/VLMs and implement other models for complex scenario generation workflows, leveraging techniques like advanced prompting, agentic tool use, and more. </li><li>Contribute to tooling for AI-based scenario understanding and validation.</li><li>Collect data and design metrics to drive business intelligence, product iteration, and model fine-tuning.</li><li>Collaborate directly with internal customers and partner teams to provide generative AI solutions for their test creation workflows.</li><li>Directly contribute to the safety and reliability of Zoox's autonomous software.</li></ul><h3>Qualifications</h3><ul><li>MS or PhD in Computer Science, Machine Learning, or related field</li><li>2+ years of industry experience in Machine Learning</li><li>Solid understanding of LLM or NLP concepts</li><li>Proficiency in Python and ML libraries (PyTorch, NumPy) demonstrated through professional or research projects</li></ul><h3>Bonus Qualifications</h3><ul><li>Practical experience in dataset creation for fine-tuning, system integration of ML models into production, or optimization techniques for low-latency inference systems</li><li>Familiarity with autonomous vehicles, robotics, and/or complex simulation environments</li><li>Hands-on experience in areas like program synthesis, diffusion models, and/or formal methods/V&amp;V</li><li>Relevant publications in conferences (e.g., CVPR, ICCV, RSS, and/or ICRA)</li></ul><br>

<p>Zoox is developing the first fully electric autonomous vehicles to support low-carbon transportation and to decrease congestion and reliance on individual car ownership in urban areas.&nbsp;</p>

Machine Learning Engineer, Simulation Scenario Generation

<div><span>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.</span></div><br><br><h3>In this role, you will:</h3><ul><li>Integrate and validate LLMs/VLMs and implement other models for complex scenario generation workflows, leveraging techniques like advanced prompting, agentic tool use, and more.&nbsp;</li><li>Contribute to tooling for AI-based scenario understanding and validation.</li><li>Collect data and design metrics to drive business intelligence, product iteration, and model fine-tuning.</li><li>Collaborate directly with internal customers and partner teams to provide generative AI solutions for their test creation workflows.</li><li>Directly contribute to the safety and reliability of Zoox's autonomous software.</li></ul><h3>Qualifications</h3><ul><li>MS or PhD in Computer Science, Machine Learning, or related field</li><li>2+ years of industry experience in Machine Learning</li><li>Solid understanding of LLM or NLP concepts</li><li>Proficiency in Python and ML libraries (PyTorch, NumPy) demonstrated through professional or research projects</li></ul><h3>Bonus Qualifications</h3><ul><li>Practical experience in dataset creation for fine-tuning, system integration of ML models into production, or optimization techniques for low-latency inference systems</li><li>Familiarity with autonomous vehicles, robotics, and/or complex simulation environments</li><li>Hands-on experience in areas like program synthesis, diffusion models, and/or formal methods/V&amp;V</li><li>Relevant publications in conferences (e.g., CVPR, ICCV, RSS, and/or ICRA)</li></ul><br>

Machine Learning Engineer, Simulation Scenario Generation

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Zoox

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