Machine Learning Engineer II - Autonomous Driving Performance Evaluation

Summary

May Mobility develops autonomous vehicles and mobility services designed to make transportation safer, greener, and more accessible. The Machine Learning Engineer II will measure, analyze, and improve the performance of the autonomous driving stack by developing ML metrics, evaluation pipelines, testing suites, and data-centric model improvement strategies.

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

Skills

• Designing quantitative metrics and statistical analyses that translate model behavior into actionable, decision-grade signals (significance, slicing, long-tail analysis)
• Building evaluation and analytics frameworks in production, including dataset slicing, result aggregation and dashboarding at scale
• Applying data-centric ML methods such as hard-example mining, resampling/reweighting and curriculum or balance adjustments to lift model performance
• 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
• Prolonged sitting
• Prolonged standing
• Prolonged computer use
• Travel required? - Low 5-10%
• 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)

Qualifications

Must Haves

• Designing quantitative metrics and statistical analyses that translate model behavior into actionable, decision-grade signals (significance, slicing, long-tail analysis)
• Building evaluation and analytics frameworks in production, including dataset slicing, result aggregation and dashboarding at scale
• Applying data-centric ML methods such as hard-example mining, resampling/reweighting and curriculum or balance adjustments to lift model performance
• 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
• Prolonged sitting
• Prolonged standing
• Prolonged computer use
• Travel required? - Low 5-10%

Nice to Haves

• 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)

Benefits

• 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