Machine Learning Engineer II - Autonomous Driving Training Infrastructure

May Mobility is transforming cities through autonomous technology to create a safer, greener, more accessible world. They are seeking ML-Oriented Software Engineers with experience in robotics applications to design and operate pipelines for their Autonomous Driving ML team, enhancing their Autonomous Driving stack. The role involves transitioning ML models to commercial scale and managing data training and evaluation workflows.

Responsibilities:

• 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

Requirements:

• 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
• 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)