Machine Learning Engineer, Simulation Scenario Generation

Number of employees

4100

Foster City, CA, United States

Posted on: 2025-09-29

Category: emobility

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Employment type:

Full time

Experience required:

Intermediate

Salary

Salary not provided

About the company:

Zoox is transforming mobility-as-a-service by developing a fully autonomous, purpose-built fleet designed for AI to drive and humans to enjoy.


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 field
  • 2+ years of industry experience in Machine Learning
  • Solid understanding of LLM or NLP concepts
  • Proficiency in Python and ML libraries (PyTorch, NumPy) demonstrated through professional or research projects
  • Bonus Qualifications

  • Practical experience in dataset creation for fine-tuning, system integration of ML models into production, or optimization techniques for low-latency inference systems
  • Familiarity with autonomous vehicles, robotics, and/or complex simulation environments
  • Hands-on experience in areas like program synthesis, diffusion models, and/or formal methods/V&V
  • Relevant publications in conferences (e.g., CVPR, ICCV, RSS, and/or ICRA)
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