Application Guide
How to Apply for Machine Learning Engineer II - Autonomous Driving & Inference Runtime
at May Mobility
🏢 About May Mobility
May Mobility is a leader in autonomous electric vehicle technology, focused on creating safe, sustainable, and eco-friendly transportation solutions. Unlike many AV companies, May Mobility prioritizes practical deployment in urban environments, with a strong emphasis on safety and community impact. Working here means contributing to a mission-driven team that values innovation and real-world application.
About This Role
As a Machine Learning Engineer II on the Autonomous Driving & Inference Runtime team, you will own the end-to-end model-compilation and deployment pipeline, ensuring that ML models run efficiently on GPU hardware. Your work directly impacts the latency and throughput of perception and planning systems, making autonomous driving safe and responsive. This role is critical for bridging the gap between model development and real-time performance in the vehicle.
💡 A Day in the Life
Your day might start by reviewing latency regression test results from overnight runs, then diving into a CUDA kernel optimization to reduce a bottleneck in the perception pipeline. You'll collaborate with perception and planning engineers to understand their model requirements and trade-offs, and end the day by deploying a new model version to a test vehicle for on-road validation.
🚀 Application Tools
🎯 Who May Mobility Is Looking For
- Has 2+ years of experience writing production-level C/C++/CUDA code for GPU-accelerated ML inference.
- Deeply understands PyTorch model optimization techniques (quantization, pruning, TensorRT, etc.) and can profile/optimize for latency and throughput.
- Familiar with autonomous driving perception and planning concepts (e.g., object detection, sensor fusion, path planning) and their computational requirements.
- Comfortable working in Linux environments and debugging complex build/deployment pipelines with tools like CMake, Docker, and CI/CD.
📝 Tips for Applying to May Mobility
Highlight any experience with TensorRT, ONNX Runtime, or similar inference optimization frameworks in your resume and cover letter.
Quantify your impact: e.g., 'Reduced inference latency by 30% through CUDA kernel optimization' or 'Owned deployment pipeline serving 10+ models in real-time.'
Mention specific autonomous driving projects or internships, even if academic, to show domain knowledge.
Tailor your resume to emphasize end-to-end ownership of pipelines, not just model training.
Include links to GitHub repos or projects demonstrating optimization work, especially if they involve CUDA or inference engines.
✉️ What to Emphasize in Your Cover Letter
['Emphasize your experience with model compilation and deployment on GPU hardware, especially for real-time systems.', 'Show how your work has improved latency/throughput budgets in previous roles or projects.', "Express genuine interest in autonomous driving and May Mobility's focus on safety and sustainability.", 'Mention familiarity with perception/planning stacks and how your optimization work directly impacts those modules.']
Generate Cover Letter →🔍 Research Before Applying
To stand out, make sure you've researched:
- → Read May Mobility's blog posts or tech talks about their autonomy stack and deployment challenges.
- → Familiarize yourself with their specific vehicle platforms and sensor suite (e.g., LiDAR, cameras, radar).
- → Look into their safety case approach and how inference runtime reliability fits into safety certifications.
- → Understand the company's current deployment cities and any public partnerships (e.g., with transit agencies).
💬 Prepare for These Interview Topics
Based on this role, you may be asked about:
⚠️ Common Mistakes to Avoid
- Don't focus only on model training; this role is about deployment and optimization, not building new models.
- Avoid vague statements like 'optimized performance' without specific metrics or techniques.
- Don't neglect to mention Linux and C++ proficiency; many ML engineers focus on Python only.
📅 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!