How to apply for Machine Learning Engineer II - Autonomous Driving Training Infrastructure

About May Mobility

May Mobility builds autonomous electric vehicles and operates them as shuttle services in cities. Its Multi-Policy Decision Making (MPDM) system is a distinct approach to AV decision-making, developed by roboticists with decades of field experience. Since 2017, the company has given over 500,000 autonomous rides, and it is based in Ann Arbor, Michigan.

About the role

This role is a machine learning engineer position focused on the training infrastructure that supports autonomous driving models. You would build and maintain systems that let ML models be trained, evaluated, and deployed for AVs. The work directly supports the MPDM technology and May Mobility's deployed shuttle services.

A typical day

A typical day might involve writing and debugging code for training pipelines, working with large datasets from AV sensors, and collaborating with robotics engineers on model evaluation. You would also spend time monitoring training jobs, improving infrastructure reliability, and coordinating remotely with the team. Exact daily routines are not stated in the job details, so ask about team structure and sprint cadence during interviews.

Who May Mobility is looking for

• Has experience as an ML-oriented software engineer, ideally in robotics or autonomous vehicles.
• Can build and maintain training pipelines, data infrastructure, and model evaluation tooling for ML at scale.
• Understands the constraints of autonomous driving systems, such as real-time inference, sensor data, and safety requirements.
• Is comfortable working remotely in the US and collaborating with robotics engineers and software teams.

Tips for this application

• Tailor your resume to show ML training infrastructure work: pipelines, data processing, distributed training, and model deployment, not just model research.
• Mention any experience with autonomous vehicles, robotics, or sensor data (LiDAR, camera, radar) because May Mobility is a robotics company first.
• Reference May Mobility's MPDM technology in your application to show you understand their specific approach to AV decision-making.
• Highlight remote work experience and ability to collaborate across time zones, since this is a US remote role.
• If you have worked on safety-critical or real-time ML systems, make that explicit because AV training infrastructure must support deployed vehicles.

What to cover in your cover letter

['Your hands-on experience building ML training infrastructure, including data pipelines, distributed training, and evaluation frameworks.', 'Any direct exposure to autonomous driving, robotics, or AV sensor data, and how that shaped your engineering decisions.', "Why May Mobility's MPDM approach and its focus on real deployed shuttle services appeal to you specifically.", 'How you would support a remote robotics engineering team and contribute to training infrastructure that serves real vehicles on the road.']

Research before applying

• Read about May Mobility's Multi-Policy Decision Making (MPDM) technology and how it differs from end-to-end or modular AV stacks.
• Look up May Mobility's deployed shuttle services and cities to understand the real-world context your training infrastructure would support.
• Check the backgrounds of May Mobility's founding team and engineering leadership, since they come from robotics and AV fields.
• Find any public talks, papers, or blog posts from May Mobility engineers about their ML and autonomy stack.

Likely interview topics

Based on the job description, expect questions about:

• How you would design a training pipeline for autonomous driving models that handles large-scale sensor data and frequent retraining.
• Your experience with distributed training frameworks and how you debug performance bottlenecks in ML training jobs.
• How you think about model evaluation and versioning for safety-critical AV systems.
• What you know about May Mobility's MPDM technology and how training infrastructure supports it.
• How you collaborate with robotics engineers and software teams in a remote setting to ship ML infrastructure.

Common mistakes to avoid

• Applying with a resume focused only on model research or Kaggle competitions without showing production ML infrastructure experience.
• Ignoring the autonomous driving context and treating this as a generic ML engineer role.
• Failing to mention any experience with robotics, sensor data, or safety-critical systems, which are central to this position.