Machine Learning Engineer, Simulation Scenario Generation

Job Description

Job Description

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)

Base Salary Range

 

There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.

 

Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.

About Zoox

Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.

Follow us on LinkedIn

Accommodations

If you need an accommodation to participate in the application or interview process please reach out to View email address on ziprecruiter.com or your assigned recruiter.

A Final Note:

You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.