Machine Learning Engineer, Simulation Scenario Generation

zoox

4100

Foster City, CA, United States

Posted on: 2025-09-29

Category: emobility

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Expired

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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