Lyft
Staff Applied Scientist, Tech Lead, Rider Applied AI
Lyft, San Francisco, California, United States, 94199
Staff Applied Scientist, Tech Lead, Rider Applied AI
We are looking for a
Staff Applied Scientist, Tech Lead, Rider Applied AI
to help build the next generation of AI/ML solutions that power personalized rider rideshare experiences. In this role, you will lead a group of scientists and analysts solving 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 revenue growth.
We are seeking a candidate who 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.
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 power rider core product experience. Drive algorithmic innovation by introducing new techniques.
Partner deeply with Product, Design, and Engineering to define the 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 Ph.D. 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.
Experience 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.
Experience 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 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
Seniority Level Mid‑Senior level
Employment Type Full‑time
Job Function Engineering and Information Technology
Equal Opportunity Employer Lyft is an equal‑opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regard 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 laws.
Hybrid Work 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. Hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year.
Compensation 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.
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Staff Applied Scientist, Tech Lead, Rider Applied AI
to help build the next generation of AI/ML solutions that power personalized rider rideshare experiences. In this role, you will lead a group of scientists and analysts solving 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 revenue growth.
We are seeking a candidate who 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.
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 power rider core product experience. Drive algorithmic innovation by introducing new techniques.
Partner deeply with Product, Design, and Engineering to define the 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 Ph.D. 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.
Experience 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.
Experience 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 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
Seniority Level Mid‑Senior level
Employment Type Full‑time
Job Function Engineering and Information Technology
Equal Opportunity Employer Lyft is an equal‑opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regard 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 laws.
Hybrid Work 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. Hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year.
Compensation 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.
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