Senior Product Manager, Rider ML/AI

Lyft•San Francisco, CA

7h•$148,000 - $185,000•Hybrid

About The Position

At Lyft, the Rider Recommendations team is an Applied AI and Machine Learning team that personalizes real-time decisions throughout the Rider app, including search, ride modes, content selection and ranking, and message generation. The team serves as an orchestration layer that determines what riders see and works closely with the Rider App team to expand personalization and real-time decision-making. The role partners with machine learning and AI engineers, data scientists, and stakeholder teams across Lyft to identify valuable outcomes and translate them into an end-to-end objective for the applications, balancing short- and long-term tradeoffs and partner-team objectives. The Senior Product Manager will own the roadmap for applied ML and AI products across the Rider app and conversion funnel, and will be accountable for team metrics, KPIs, and financial outcomes. The role includes shaping model objectives, defining and evaluating rider lifetime value, planning effective cold starts for new regions, modes, and features, and helping create a unified Rider profile for ML and AI training. The position also involves writing product requirements and planning documents, designing and analyzing experiments, monitoring model performance, and communicating progress and roadblocks to nontechnical leadership. This is a hybrid role requiring work in the office three days per week—Mondays, Wednesdays, and Thursdays—with flexibility to work from anywhere for up to four weeks per year.

Requirements

Experience driving technically complex machine-learning products end to end, from inception through launch, significant user adoption, and sunset; consumer-facing applications involving UI interactions are ideal.
Experience managing a team of software engineers from a product perspective and tracking roadmap progress using tools such as Jira, Linear, spreadsheets, or ClickUp.
Experience with large-scale, user-facing experimentation platforms such as Optimizely, Statsig, or Eppo, and understanding of concepts such as statistical power and significance.
Experience using AI agents to accelerate day-to-day work, including understanding code, querying company data, and preparing leadership dashboards.
Knowledge of machine learning and AI, including commonly used machine-learning and deep-learning packages such as XGBoost, CatBoost, LightGBM, scikit-learn, Keras, and PyTorch, as well as the autoresearch approach.
Familiarity with preparing and leading presentations for small CXO-level groups and large all-hands audiences of 100 or more people at medium-to-large companies.
Ability to review and provide critical product feedback on technical specifications written by engineers.
Ability to drive results independently with little supervision in flat-structured organizations where approvals are not needed but results are closely monitored.

Responsibilities

Define, own, and manage the roadmap for applied ML and AI products across every screen of the Rider application and throughout the Rider conversion funnel.
Define, track, and periodically report team metrics and KPIs, with direct responsibility for the financial outcomes the team drives.
Define the objective function to optimize across the Rider app, accounting for short-term and long-term goals and stakeholder objectives, including Loyalty, Partnership, Ads, Business, AV, Safety, and Luxury.
Work with data science to define, measure, and predict the long-term value of Riders.
Identify how to cold start new region, mode, and feature launches to drive adoption while maintaining algorithm performance.
Work cross-functionally to develop a unified Rider profile vision by identifying, collecting, and organizing Rider information for ML and AI training.
Write detailed product requirements, strategy, and planning documents for technical and nontechnical audiences to align product development, set milestone expectations, and create predictable workstream outcomes.
Design, run, and analyze experiments and ongoing evaluation systems to monitor model performance and assess improvements.
Present monthly team progress to nontechnical leadership, highlighting progress and roadblocks.

Benefits

Medical, dental, and vision insurance options, with additional programs available when enrolled.
Mental health benefits.
Family-building benefits.
Child-care and pet benefits.
401(k) plan with company match.
Twelve observed holidays.
Discretionary paid time off for salaried team members; hourly team members receive 15 days of paid time off.
Eighteen weeks of paid parental leave for biological, adoptive, and foster parents.
Subsidized commuter benefits.
Monthly Lyft credits and complimentary Lyft Pink membership.
Hybrid roles may work from anywhere for up to four weeks per year.