Senior Machine Learning Engineer, Experimentation

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 Experimentation team is the foundation for decision-making across Lyft. We build state-of-the-art randomized, non-randomized, and adaptive measurement methodologies and run a powerful platform that measures the impact of every product change.

We are seeking an experienced ML Engineer to drive the development and scaling of our core systems for adaptive experimentation, AI, and pre-experimental measurement. You'll partner closely with product, engineering, and business leaders to build and scale our products and systems.

To learn more about the Experimentation team’s work, see Parameter Exploration at Lyft , Addressing Interference Bias , and Challenges in Experimentation.

Responsibilities:

As an ML Engineer focused on the MAB platform and AI roadmap, you will be primarily responsible for building and productionizing advanced machine learning systems.

• Partner with Engineers, Data Scientists, Product Managers, and Business Partners to apply machine learning for business and user impact


• Perform data analysis and build proof-of-concept to explore and propose ML solutions to both new and existing problems


• Develop statistical, machine learning, or causal models


• Write production quality code to launch machine learning models at scale


• Evaluate machine learning systems against business goal


• Participating in team on-call rotation to ensure the real time health of experimentation systems

Experience:

• Advanced degree in a quantitative field like computer science, statistics, economics, or engineering, or relevant work experience.


• 4+ years of hands-on industry experience in machine learning engineering, data science, or causal inference.


• Demonstrated experience in building, deploying, and scaling ML models and pipelines in a production environment.


• Experience with multi-arm bandit (MAB) and LLM frameworks is highly preferred.

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 $162,800 - $203,500. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Range is not inclusive of potential equity offering, bonus or benefits. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

Total compensation is 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.