Machine Learning Engineer II - Autonomous Driving Performance Evaluation

Back to jobs Machine Learning Engineer II - Autonomous Driving Performance Evaluation USA - Remote Apply May Mobility is transforming cities through autonomous technology to create a safer, greener, more accessible world. Based in Ann Arbor, Michigan, May develops and deploys autonomous vehicles (AVs) powered by our innovative Multi-Policy Decision Making (MPDM) technology that literally reimagines the way AVs think. Our vehicles do more than just drive themselves - they provide value to communities, bridge public transit gaps and move people where they need to go safely, easily and with a lot more fun. We’re building the world’s best autonomy system to reimagine transit by minimizing congestion, expanding access and encouraging better land use in order to foster more green, vibrant and livable spaces. Since our founding in 2017, we’ve given more than 500,000 autonomous rides to real people around the globe. And we’re just getting started. We’re hiring people who share our passion for building the future, today, solving real-world problems and seeing the impact of their work. Join us. Job Summary 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 ML Engineering concepts to measure, analyze and systematically improve the performance of May's Autonomous Driving stack through data, metrics, evaluation and test/hillclimbing suites. Essential 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 and Abilities Success in this role typically requires the following competencies: - 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. Qualifications and Experience Candidates most successful in this role typically hold the following qualifications or comparable knowledge or experience: Required - 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. Desirable - 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).