Machine Learning Engineer II - Autonomous Driving Training Infrastructure

May Mobility is entering an exciting phase of growth as we expand our first-of-its-kind autonomous shuttle and mobility services across the nation. Launched in 2017 with a strong team of experienced roboticists and software engineers with decades of experience fielding robotic systems in the wild, May Mobility is looking to expand its team of robotics engineers with a background in robotics or autonomous vehicles.

We are seeking ML-Oriented Software Engineers with experience in robotics applications. As part of our Autonomous Driving ML team, you will use your knowledge of Software and ML concepts to design and operate pipelines that allow May’s Autonomous Driving stack to improve quickly and reliably at scale. Essential Responsibilities

• Architect and operate data and training pipelines across cloud and cluster environments.
• Build and maintain distributed training and orchestration tooling.
• Design and maintain the data and metadata stores that back our training and evaluation workflows

Skills and Abilities

Success in this role typically requires the following competencies:

• Architect data and model parallelism training infrastructure for large data (>100TB) or large model (>100GB) applications
• Architecting and operating containerized/pipelined ML Training workloads, including GPU scheduling/autoscaling, dataloader design and experiment tracking.
• Building and maintaining CI/CD pipelines and infrastructure-as-code (e.g. Terraform).
• Working with relational and object stores, and high-throughput data formats for ML workloads.

• Bachelor’s or Master’s degree in Robotics, Computer Science or a related field with strong mathematical and engineering foundations.
• A minimum of 2 years building ML-oriented infrastructure, platforms, or distributed systems in production.
• Proficiency in C++, Python and PyTorch with experience in Linux environments.
• Familiarity with basic concepts in Machine Learning (training loops, basic operators and architectures)

Desirable

• Proficiency in Go or Rust.
• Familiarity with ML orchestration and experiment tooling such as Ray, Kubeflow, Airflow, MLflow, or Weights & Biases.
• Familiarity with distributed training frameworks (PyTorch DDP/FSDP, DeepSpeed).
• Familiarity with data pipeline and storage technologies (Spark, Parquet, object storage, feature/metadata stores).
• Familiarity with basic Perception and Planning concepts in Autonomous Driving.

Physical Requirements

• Standard office working conditions which includes but is not limited to:

• Prolonged sitting
• Prolonged standing
• Prolonged computer use

Benefits and Perks

• Comprehensive healthcare suite including medical, dental, vision, life, and disability plans. Domestic partners who have been residing together at least one year are also eligible to participate.
• Health Savings and Flexible Spending Healthcare and Dependent Care Accounts available.
• Rich retirement benefits, including an immediately vested employer safe harbor match.
• Generous paid parental leave as well as a phased return to work.
• Flexible vacation policy in addition to paid company holidays.
• Total Wellness Program providing numerous resources for overall wellbeing

May Mobility is transforming cities through autonomous technology to create a safer, greener, more accessible world. Based in Ann Arbor, Michigan, May develops and deploys autonomous vehicles (AVs) powered by our innovative Multi-Policy Decision Making (MPDM) technology that literally reimagines the way AVs think.

Our vehicles do more than just drive themselves - they provide value to communities, bridge public transit gaps and move people where they need to go safely, easily and with a lot more fun. We’re building the world’s best autonomy system to reimagine transit by minimizing congestion, expanding access and encouraging better land use in order to foster more green, vibrant and livable spaces. Since our founding in 2017, we’ve given more than 500,000 autonomous rides to real people around the globe. And we’re just getting started. We’re hiring people who share our passion for building the future, today, solving real-world problems and seeing the impact of their work. Join us.