Application Guide

How to Apply for Machine Learning Engineer II

at May Mobility

🏢 About May Mobility

May Mobility is pioneering autonomous electric vehicle technology with a focus on safety, sustainability, and accessibility. Their small, nimble team offers engineers the chance to directly impact real-world autonomous driving systems, from data pipelines to deployment.

About This Role

This role is critical for building the infrastructure that powers May Mobility's ML training and evaluation. You'll architect data pipelines, distributed training systems, and metadata stores, enabling rapid iteration on self-driving algorithms. Your work directly accelerates the development of safe, reliable autonomous vehicles.

💡 A Day in the Life

You'll start by reviewing pipeline health dashboards and logs, then collaborate with ML researchers to prioritize new data ingestion or training jobs. After lunch, you might debug a distributed training job, write a new C++ component for data preprocessing, or design a metadata schema for evaluation results. The day ends with a standup to sync with the team on progress and blockers.

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Interview Prep

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🎯 Who May Mobility Is Looking For

  • Experienced in building production-grade ML infrastructure, particularly distributed training pipelines on cloud and cluster environments (e.g., AWS, Kubernetes, Slurm).
  • Proficient in C++ and Python, with deep experience in PyTorch and Linux systems; able to optimize performance and debug complex distributed systems.
  • Familiar with ML concepts like model training, evaluation, and data management, but with a primary focus on the engineering side rather than research.
  • Comfortable working in a fast-paced startup environment, owning end-to-end solutions from design to deployment, and collaborating with both ML and software engineering teams.

📝 Tips for Applying to May Mobility

1

Highlight specific projects where you built or scaled ML training pipelines, including metrics like throughput, latency, or data volume handled.

2

Demonstrate your C++ proficiency with examples of performance-critical code, such as custom operators or low-latency data processing.

3

Mention experience with any autonomous vehicle or robotics data pipelines, especially with sensor data (LiDAR, cameras) and annotation workflows.

4

Show familiarity with May Mobility's technology by referencing their approach to autonomous driving (e.g., modular vs. end-to-end, safety-first).

5

Prepare a brief portfolio or GitHub repo showcasing your infrastructure work, with clear documentation on architecture and trade-offs.

✉️ What to Emphasize in Your Cover Letter

["Emphasize your passion for autonomous vehicles and sustainable transportation, linking it to May Mobility's mission.", 'Detail your experience with distributed training and orchestration tooling, mentioning specific technologies (e.g., Ray, Kubeflow, Docker).', "Explain how you've designed data and metadata stores (e.g., feature stores, experiment tracking) to support ML workflows.", 'Highlight your ability to work cross-functionally with ML researchers and software engineers to iterate on infrastructure.']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Read about May Mobility's autonomous driving technology stack, including their sensor suite and decision-making approach.
  • → Understand their focus on safety and sustainability; review any public safety reports or partnerships (e.g., with cities or transit authorities).
  • → Check out their blog or engineering talks (if any) to learn about their culture and technical challenges.
  • → Research their competitors (e.g., Waymo, Cruise) to understand May Mobility's unique value proposition and market position.

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Design a distributed training pipeline for a large-scale model; discuss data sharding, fault tolerance, and resource scheduling.
2 How would you optimize a PyTorch training script for multi-GPU or multi-node environments? Discuss mixed precision, gradient accumulation, etc.
3 Describe your experience with C++ in an ML context; when would you choose C++ over Python for a pipeline component?
4 Given a dataset of sensor logs, how would you design a metadata store to enable efficient querying for training and evaluation?
5 How do you ensure reproducibility and versioning in ML experiments? Walk through your ideal tooling and workflow.
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⚠️ Common Mistakes to Avoid

  • Focusing too much on ML model building rather than infrastructure; this role is about pipelines and systems, not model development.
  • Underestimating the importance of C++ proficiency; be ready to discuss low-level optimizations and memory management.
  • Not showing understanding of production environments; avoid generic academic projects without discussing scalability, reliability, or monitoring.

📅 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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