Staff Applied Scientist, Tech Lead, Rider Applied AI

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.

Why Join Rider Applied Ai Team?

The Rider team is the heartbeat of Lyft business. Every feature you build translates to a better experience for millions of riders globally. The Rider Applied AI science team’s mission is to architect the intelligent layer of the Rider experience through applying state-of-the-art AI techniques and deliver hyper-personalized experiences for every rider, every ride. You will have the autonomy of a startup lead with the data scale of a global tech giant.

We are seeking a Staff Applied Scientist to help build the next generation of AI/ML solution that powers personalized rider rideshare experience. In this role, you will lead a high-impact group of scientists and analysts to solve complex, large-scale problems in content ranking, user journey optimization and Rider intelligence engine. You’ll collaborate closely with Engineering, Product, Data Science, and Design to translate ambiguous business problems into rigorous algorithmic solutions that improve rider experience and drive meaningful revenue growth.

This is a high-impact, highly technical role within a core business line. The ideal candidate brings strong applied machine learning intuition, hands-on modeling experience, and the ability to write clean, efficient production code. You will play a critical role in shaping the future of the Lyft rideshare experience and pushing the boundaries of personalization, measurement, and real-time optimization in a dynamic marketplace and bridge the gap between cutting-edge applied science research and production-scale product impact.

Responsibilities

Own complex, open-ended problem spaces. Translate vague business problems into concrete mathematical objectives.

Lead multiple high-impact Machine Learning and AI initiatives that powers rider core product experience. Drive algorithmic innovation by introducing new techniques.

Partner deeply with Product, Design and Engineering to define technical vision for how AI will transform Lyft rider experience.

Design, develop, and deploy advanced machine learning, optimization, and decisioning algorithms for large-scale real-time inferences, balancing scientific rigor with practical engineering constraints.

Establish robust evaluation frameworks, defining offline metrics, calibration checks, counterfactual methods, experiment designs, and long-term measurement strategies to ensure model correctness and system stability.

Build reusable modeling infrastructure, libraries, and best practices, enabling faster iteration and higher modeling quality across the broader Rider Science and Engineering teams.

Mentor and guide junior/mid-level scientists, serving as a technical advisor on modeling design, experimentation, code quality, and scientific reasoning.

Experience

Master’s or PhD in Machine Learning, Computer Science, Optimization, Statistics, Engineering, Applied Mathematics, or a related quantitative field; or equivalent high-impact industry experience.

5+ years of applied science or machine learning experience, with a track record of deploying production models that drive measurable business outcomes.

Experienced in owning multi-project modeling scope across ambiguous problem spaces and integrating work across engineering, product, and data science partners.

Strong proficiency in Python, ML frameworks and distributed data systems

Experienced in defining and executing offline and online evaluation strategies, including experiment design, counterfactual analysis, and diagnostics for system failures.

Strong technical leadership skills — able to align partners, influence technical architecture, challenge assumptions, and guide cross-team modeling decisions.

Experience in mentoring other scientists, elevating technical quality, and improving modeling/analysis standards across the team.

Excellent communication skills, with the ability to articulate complex modeling concepts, system trade-offs, and scientific reasoning to both technical and business stakeholders.

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 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

Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program

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 $176,000 - $220,000, 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.