Application Guide

How to Apply for Machine Learning Engineer II - Autonomous Driving Performance Evaluation

at May Mobility

🏢 About May Mobility

May Mobility is a leader in autonomous vehicle technology, focusing on safe, sustainable, and eco-friendly transportation. Unlike many AV companies, they prioritize deployment in controlled environments (e.g., campuses, downtown areas) and emphasize safety and community impact. Working here means contributing to real-world autonomous mobility solutions that are already on the road.

About This Role

As an ML Engineer II in Performance Evaluation, you will own the metrics and evaluation pipelines that directly determine whether ML models are safe and effective for deployment. Your work will bridge offline testing, simulation, and on-road performance, ensuring that every model release is rigorously validated. This role is critical for maintaining the high safety standards of May Mobility's autonomous driving system.

💡 A Day in the Life

A typical day might start with reviewing automated test results from overnight runs, then diving into a regression to identify root cause using data analysis. After lunch, you could collaborate with perception or planning teams to design new evaluation metrics for a model update, and end the day by updating a hillclimbing suite to track performance improvements.

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Interview Prep

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🎯 Who May Mobility Is Looking For

  • You have at least 2 years of hands-on experience building evaluation or data analysis systems for machine learning in production, preferably in autonomous driving or robotics.
  • You are highly proficient in Python with NumPy/Pandas and have worked extensively in Linux environments, automating pipelines and analyzing large datasets.
  • You understand ML fundamentals and have familiarity with autonomous driving perception and planning concepts, enabling you to interpret model behavior and identify regressions.
  • You are detail-oriented and systematic, capable of designing comprehensive test suites and root-causing issues from metrics to model code.

📝 Tips for Applying to May Mobility

1

Tailor your resume to highlight specific projects where you built evaluation pipelines or metrics for ML models; include quantifiable results (e.g., 'reduced regression detection time by 30%').

2

In your cover letter, mention your experience with simulation environments (e.g., CARLA, SUMO) or on-road data collection, as these are directly relevant to the role.

3

Showcase your ability to work with large-scale datasets and automated triage systems—mention any tools you've built for error analysis or data curation.

4

If you have experience with autonomous driving stacks (e.g., perception, planning), explicitly list the algorithms you've worked with (e.g., object detection, trajectory prediction).

5

Prepare a portfolio or GitHub repo with examples of your evaluation frameworks, especially if they involve regression testing or hill climbing.

✉️ What to Emphasize in Your Cover Letter

['Emphasize your passion for safe autonomous driving and how your evaluation work directly contributes to safety.', 'Highlight your experience with building automated test suites and regression detection systems for ML models.', "Mention your familiarity with May Mobility's approach (e.g., their focus on low-speed, controlled environments) and how your skills align with their deployment strategy.", 'Discuss a specific example where your evaluation insights led to model improvements or prevented a regression.']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Read May Mobility's blog and press releases to understand their current deployments, vehicle types, and safety philosophy.
  • → Look into their technology stack—any public info on their perception, planning, or simulation tools (e.g., do they use CARLA, in-house sim?).
  • → Research their competitors (e.g., Waymo, Cruise) and understand what makes May Mobility's approach different (e.g., focus on shared autonomous shuttles).
  • → Check their careers page for any technical talks or presentations by their engineering team to get insight into their culture and challenges.

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Design an evaluation pipeline for a new perception model: what metrics would you track, and how would you set up regression tests?
2 How would you triage a regression in on-road performance? Walk through your process from detecting the issue to root cause.
3 Explain a time you used error mining or data balancing to improve model performance. What techniques did you use?
4 Given a dataset with imbalanced classes (e.g., rare pedestrian scenarios), how would you curate evaluation sets to ensure robust testing?
5 How do you ensure your evaluation pipeline is reproducible and scalable across different environments (simulation vs. on-road)?
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⚠️ Common Mistakes to Avoid

  • Don't submit a generic resume that doesn't highlight evaluation or metrics work; this role is specifically about building those systems.
  • Avoid overstating your autonomous driving experience if you don't have it—instead, emphasize transferable skills from other ML domains.
  • Don't neglect the importance of software engineering best practices (e.g., version control, testing, CI/CD) in your application; this role requires robust pipeline engineering.

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to May Mobility!

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