Senior Data Scientist II (ML)

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.

Senior ML Scientist II

May Mobility is experiencing a period of significant growth as we expand our autonomous shuttle and mobility services nationwide. We are seeking talented data scientists and machine learning engineers to develop automated methods for tagging data collected by our autonomous vehicles. This will enable us to generate valuable insights from our data, making it easily searchable for triaging issues, creating test sets, and building datasets for autonomy improvements. Join us and make a crucial impact on our development and business decisions!

Responsibilities

• Design, implement, and deploy state-of-the-art machine learning models for analyzing multimodal data to generate searchable metadata and facilitating downstream engineering workflows such as quick issue triaging.
• Curate high-quality datasets for evaluation and training to ensure model robustness, performance, and coverage.
• Research and implement novel techniques for sequential feature extraction, weak supervision, and self-supervised learning to efficiently handle long-tail events and continuously improve labeling data quality.
• Establish and maintain frameworks for model validation and performance monitoring to drive continuous improvement.

Skills

Success in this role typically requires the following competencies:

• Expert proficiency in designing and implementing deep learning architectures for multimodal data for offline analysis.
• Strong understanding of data labeling best practices, label consistency, and performance metrics specifically relevant to large-scale auto-tagging accuracy and dataset curation.
• Expertise in machine learning, with hands-on experience in the design, training, and evaluation of a wide range of algorithms.
• Awareness of the latest advancements in the field, with the ability to translate innovative concepts into practical solutions for May.
• Excellent problem-solving skills with a meticulous approach to model architecture and optimization.

Qualifications and Experience

Required

• B.S, M.S. or Ph.D. Degree in Engineering, Data Science, Computer Science, Math, or a related quantitative field.
• 5+ years of hands-on experience as a Data Scientist or ML Engineer with a strong focus on algorithm design and machine learning.
• Experience working with multimodal data like visual data (images/video), structured perception and behavior outputs (e.g., agent tracks, vehicle state estimation, motion planner outputs).
• Expert-level programming skills in Python with extensive use of modern deep learning frameworks like TensorFlow or PyTorch.
• Demonstrated experience in building and deploying production-level machine learning systems from conception to delivery.
• Expertise in PySpark/Apache Spark for handling large-scale data processing.

Desirable

• Background in robotics or autonomous systems.
• Experience working with multimodal data like visual data (images/video), structured perception and behavior outputs (e.g., agent tracks, vehicle state estimation, motion planner outputs).
• Solid understanding of ML deployment lifecycle, MLOps practices, and cloud computing platforms (e.g., AWS, GCP).

Benefits and Perks

• 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 pare