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 a Machine Learning Engineer II to design and operate pipelines for their Autonomous Driving ML team, focusing on improving the company's autonomous driving stack. The role involves architecting and maintaining data and training pipelines in cloud and cluster environments.

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

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)
• 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