Application Guide

How to Apply for Lead ML Engineer - Lane & Route Networking Mapping

at May Mobility

🏢 About May Mobility

May Mobility is pioneering autonomous electric vehicles with a strong emphasis on safety, sustainability, and eco-friendly transportation. Their focus on lane and route networking mapping for both offline and real-time applications makes them a leader in autonomous driving technology.

About This Role

As Lead ML Engineer, you will spearhead the development of advanced neural networks for lane and route mapping, including BEV transformers and multimodal fusion. Your work will directly impact the vehicle's perception and decision-making, ensuring safe and efficient navigation.

💡 A Day in the Life

A typical day involves leading design reviews for new neural network architectures, collaborating with perception and prediction teams on integration, and analyzing model performance on edge cases. You'll also mentor junior engineers, review code, and iterate on data pipelines to improve mapping accuracy.

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🎯 Who May Mobility Is Looking For

  • Holds a PhD or Master's in CS, EE, Robotics with a strong math background and 7+ years of industry experience deploying ML/DL models for mapping or computer vision at scale.
  • Deep expertise in vectorized mapping networks (e.g., MapTR), BEV scene representation, cross-modal fusion, transformers/GNNs for lane geometry, and computer vision foundations.
  • Proven experience architecting and implementing production-grade mapping stacks, including data pipelines for auto-labeling, synthetic data, and active learning.
  • Strong programming skills in Python and C++, with experience in frameworks like PyTorch/TensorFlow and a track record of leading technical projects and mentoring teams.

📝 Tips for Applying to May Mobility

1

Highlight your experience with vectorized mapping networks like MapTR or similar, and BEV transformers, with specific examples of deployed models.

2

Emphasize your work on production-grade mapping stacks, including integration with perception and prediction modules, and mention any metrics on performance or latency.

3

Showcase your data strategy expertise: describe projects where you built auto-labeling or active learning pipelines for long-tail scenarios.

4

Tailor your resume to mention leadership: include examples of mentoring junior engineers, driving architecture decisions, and code reviews.

5

Research May Mobility's specific approach to mapping and mention it in your cover letter to show alignment with their technology stack.

✉️ What to Emphasize in Your Cover Letter

['Demonstrate deep understanding of vectorized mapping and BEV transformers, linking your experience to their specific needs.', 'Highlight your ability to own end-to-end data strategy and production deployment, with concrete examples.', 'Showcase leadership skills and experience mentoring teams in a fast-paced autonomous driving environment.', 'Express passion for safe, sustainable autonomous transportation and how your work contributes to that mission.']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Study May Mobility's autonomous vehicle technology stack, especially their approach to mapping and perception.
  • → Read recent publications or patents from the company on lane and route networking or BEV transformers.
  • → Understand their focus on safety and sustainability, and how their vehicles are deployed in real-world environments.
  • → Look into their partnerships or pilot programs to grasp the scale and impact of their technology.

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Design a neural network architecture for real-time lane detection and vectorized mapping from multi-camera BEV input.
2 How would you handle long-tail scenarios in lane mapping, such as construction zones or unusual road markings?
3 Discuss your experience with auto-labeling and active learning for mapping data. How do you ensure label quality?
4 Explain how you would integrate your mapping module with upstream perception and downstream prediction systems.
5 Describe a time you led a major feature from inception to deployment, including architecture design and mentoring.
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⚠️ Common Mistakes to Avoid

  • Don't submit a generic application without referencing their specific mapping challenges (e.g., vectorized vs. rasterized).
  • Avoid overstating your experience; be honest about your role in deploying ML models at scale.
  • Don't neglect to mention leadership and mentorship experience; this role requires technical leadership.

📅 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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