At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

The Rider Recommendations team is an Applied AI and Machine Learning team tasked with personalizing all realtime decisions happening inside the Rider Apps, including search, ride modes, content selection and ranking, message generation and others. Since last year, this team has been one of the major contributors to financial outcomes at Lyft and, working closely with the Rider App team, they are in the process of evolving the Rider application to make more room for personalization and realtime decision making. The team operates as the orchestration layer behind everything that the rider sees, being the final decision maker on what to show.

You will work closely with machine learning, AI engineers and data scientists to define the right objectives for the team’s models, interfacing with the rest of the company to understand the Rider App’s stakeholder teams needs and Lyft’s overall goals. In order to do this, you will have to identify the outcomes worth pursuing and translate them into a single objective to be used end to end by the applications. You’ll have to consider short term and long term tradeoffs, other than objectives of partner teams.

Responsibilities:

Define, own and be responsible for the roadmap of applied ML and AI products in every screen of the Rider Application, through the Rider conversion funnel
Define, track and periodically report on metrics and KPIs of the team, being directly responsible for financial outcomes that the team drives
Define the objective function to be maximized / minimized throughout the rider app, taking into account short term, long term and stakeholder teams’ objectives (Loyalty, Partnership, Ads, Business, AV, Safety, Luxury etc)
Work with data science to define, measure and predict long term value of Riders
Identify how to cold start new launches of region, modes and features in order to effectively drive adoption while still maintaining algorithm performance
Work in a cross functional team building a unified Rider profile vision by identifying, collecting and organizing information about Riders to be used for ML and AI training
Write detailed product requirements, strategy and planning documents aimed at both technical and non technical audiences to drive alignment on product development, set expectations on delivery of milestones and create predictable outcomes for workstreams
Design, run and analyze experiments and ongoing evaluation systems to monitor model performance and evaluate improvements
Present to non technical leadership on the progress of the team monthly, showcasing progress and highlighting roadblocks

Experience:

Experience driving technically complex machine learning (ML) products end to end, from inception, to launch with significant user adoption, to sunset. Ideally the applications are consumer facing and involve UI interactions with users
Experience in managing (from product perspective) a team of software engineers, tracking the progress towards a roadmap through appropriate tools (Jira, Linear, Spreadsheets, Clickup etc)
Experience with user facing large scale experimentation (optimizely, statsig, eppo etc), knowing concepts such as power and statistical significance
Experience in using AI agents to accelerate day to day execution including, amongst others, to: understand code, perform queries on company data, prepare dashboards for leadership
Knowledge of how machine learning and AI work, including of the most widely known machine learning and deep learning packages (xgboost, catboost, lightgbm, scikit learn, keras, pytorch etc), other than the autoresearch approach
Familiarity with crafting and leading presentations to small groups at the CXO executive level and to big teams (100+ ppl) at an all hands like level in medium to large companies
Ability to review and provide critical product feedback to technical specifications written by engineers
Ease in driving results and operating independently and with little supervision in flat structured organizations where approvals are not needed but results are closely monitored

Benefits:

Great 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 to help save for your future
In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
Subsidized commuter benefits
Monthly Lyft credits and complimentary Lyft Pink membership

Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the San Francisco area is $148,000 - $185,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.