Application Guide
How to Apply for Lead Machine Learning Engineer - Localization
at May Mobility
🏢 About May Mobility
May Mobility is pioneering the deployment of autonomous electric vehicles, focusing on safe, sustainable, and eco-friendly transportation. Their unique approach to solving real-world mobility challenges with cutting-edge technology makes them a leader in the AV industry. Working here means contributing to a mission-driven team that values innovation and societal impact.
About This Role
As the Lead Machine Learning Engineer for Localization, you will architect and drive the technical roadmap for production-grade localization systems, impacting the core safety and reliability of autonomous vehicles. This role is pivotal in advancing May Mobility's autonomy stack across diverse operational design domains, ensuring robust performance in real-world conditions.
💡 A Day in the Life
You'll start your day by reviewing the team's progress on ongoing ML projects, then dive into design discussions or code reviews for new localization features. You'll collaborate with data engineers to refine data pipelines and with autonomy engineers to ensure integration, while also mentoring junior engineers and staying updated on the latest research. Your day is a mix of technical deep dives, strategic planning, and team leadership, all aimed at advancing the reliability of May Mobility's autonomous vehicles.
🚀 Application Tools
🎯 Who May Mobility Is Looking For
- Holds a Ph.D. or Master's in CS, EE, Robotics, or related field with a strong mathematical foundation and 7+ years of industry experience in ML/DL for computer vision or localization at scale.
- Deep expertise in computer vision fundamentals, including vectorized landmark detection, BEV scene representation, temporal modeling, and self-supervised learning, with a proven track record of deploying models in production.
- Experienced in leading technical teams, mentoring junior engineers, and driving projects from research to deployment with rigorous code reviews and automated testing.
- Skilled in data strategy, including data curation, auto-labeling, synthetic data generation, and active learning to handle long-tail scenarios, with a passion for robustness and edge cases.
📝 Tips for Applying to May Mobility
Highlight specific projects where you've architected and deployed localization or computer vision models at scale, emphasizing real-time performance and robustness.
Showcase your experience with sensor fusion (vision/LiDAR/radar) and how you've handled sensor degradation in your previous work.
Demonstrate your leadership by detailing how you've mentored engineers and driven technical roadmaps, with concrete examples of successful feature deployments.
Tailor your resume to include keywords from the job description such as 'BEV', 'landmark detection', 'self-supervised learning', and 'ODD' to pass ATS filters.
Include a portfolio or links to relevant publications, open-source contributions, or technical blog posts that showcase your expertise in ML for localization.
✉️ What to Emphasize in Your Cover Letter
["Emphasize your alignment with May Mobility's mission of safe and sustainable autonomous transportation and how your work can contribute to that.", "Detail your experience with production-grade ML systems, specifically in localization, and how you've overcome challenges like sensor degradation or long-tail scenarios.", "Highlight your leadership and mentoring skills, showing how you've built and guided high-performing teams to deliver complex ML features.", 'Express your passion for innovation and continuous learning, and how you stay updated with the latest advancements in computer vision and ML.']
Generate Cover Letter →🔍 Research Before Applying
To stand out, make sure you've researched:
- → Research May Mobility's current autonomous vehicle technology and their specific approach to localization and mapping.
- → Read about their deployments in different cities and ODDs to understand the diversity of environments they operate in.
- → Look into any recent publications or tech talks from May Mobility engineers about their ML stack or computer vision techniques.
- → Familiarize yourself with their safety record and how they handle edge cases, as this reflects their engineering culture.
💬 Prepare for These Interview Topics
Based on this role, you may be asked about:
⚠️ Common Mistakes to Avoid
- Avoid being vague about your experience; provide specific metrics and examples of deployed models and their impact.
- Don't overlook the importance of robustness and sensor degradation; failing to address these in your application may show a lack of understanding of real-world AV challenges.
- Avoid focusing solely on research without demonstrating production engineering skills like code reviews, testing, and scalable data pipelines.
- Don't neglect to showcase your leadership and mentoring experience; this role requires driving features and developing junior engineers.
📅 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:
Application Review
1-2 weeks
Initial Screening
Phone call or written assessment
Interviews
1-2 rounds, usually virtual
Offer
Congratulations!