Machine Learning Engineer II - Autonomous Driving Performance Evaluation

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 join their Autonomous Driving ML team, focusing on improving the performance of their Autonomous Driving stack through data metrics and evaluation. The role involves designing and implementing ML metrics, building evaluation pipelines, and driving model improvements.

Responsibilities:

• Design, implement and own ML metrics and evaluation pipelines spanning offline model evaluation, simulation and on-road performance
• Build and maintain test, regression and hillclimbing suites that gate model and stack releases, including automated triage of regressions to root cause
• Drive model improvement through loss analysis, error mining, and data balancing/curation strategies for training and evaluation sets

Requirements:

• Bachelor's or Master's degree in Robotics, Computer Science, Statistics, or a related field with strong mathematical and engineering foundations
• A minimum of 2 years building evaluation, metrics, or data analysis systems for ML in production
• Proficiency in Python (NumPy/Pandas or equivalent dataframe tooling) with experience in Linux environments
• Familiarity with basic concepts in Machine Learning (losses, train/eval splits, common failure modes) and basic Perception and Planning concepts in Autonomous Driving
• Proficiency in Go or C++
• Familiarity with experiment tracking and evaluation tooling such as MLflow, Weights & Biases, or in-house equivalents
• Familiarity with statistical methods for A/B comparison, regression detection and noisy-metric analysis
• Familiarity with data mining and curation at scale (embedding-based retrieval, active learning, auto-labeling)
• Familiarity with visualization and dashboarding tools (Plotly, Grafana, Streamlit or similar)