Machine Learning Engineer, May Mobility

• Candidates most successful in this role typically hold the following qualifications or comparable knowledge or experience:
• 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
• Don’t meet every single requirement? Studies have shown that women and/or people of color are less likely to apply to a job unless they meet every qualification. At May Mobility, we’re committed to building a diverse, inclusive, and authentic workforce, so if you’re excited about this role but your previous experience doesn’t align perfectly with every qualification, we encourage you to apply anyway! You may be the perfect candidate for this or another role at May

Desirable

• 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
• 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
• 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
• Physical Requirements:
• Standard office working conditions which includes but is not limited to:
• Prolonged sitting
• Prolonged standing
• Prolonged computer use