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.
- Own the process to transition Autonomous Driving ML models from concept to commercial scale.
- Architect and operate data, training and evaluation pipelines across cloud and cluster environments.
- Design and maintain the data and metadata stores that back our training and evaluation workflows.
- Employ data and model parallelism training techniques 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.
- 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)
- Familiarity with basic Perception and Planning concepts in Autonomous Driving.
- 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).