Lyft Verified

Transportation

Machine Learning Engineer

RemoteRemoteFull Time$140,800–$176,000 /yrPosted 2 months agoVisa sponsorship available

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Required skills

Machine Learning
TensorFlow
PyTorch
Python
Golang
Deep Learning
Recommendation Systems
Statistical Analysis
hypothesis testing
regression analysis

Role summary

Lyft is seeking a Machine Learning Engineer for its Fulfillment team in the Marketplace group. This role involves designing, developing, and deploying state-of-the-art machine learning systems for real-time applications, leveraging petabyte-scale data. Responsibilities include building ML models, implementing feature pipelines and training workflows, evaluating system performance against KPIs, and collaborating with cross-functional teams. The position requires a BS/MS in Computer Science or related field, 2+ years of ML experience, proficiency in Python/Golang, and experience with deep learning frameworks like TensorFlow or PyTorch. The role is hybrid, based in New York City, with a salary range of $140,800 - $176,000 annually.

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.

With a billion rides per year and counting, Lyft is solving hard problems in a rapidly growing domain with a lot of data and creative solutions in Rider, Driver, Marketplace, and beyond. While traditional approaches to optimization and problem decomposition are sufficient to disrupt transportation, building a next-generation platform for low-cost, ultra-immersive transportation to improve people's lives warrants modern ML utilizing petabyte-scale data. Our highly motivated Machine Learning Engineers work on these challenging problems and define solutions to directly impact various aspects of our core business.

The Fulfillment group, within the Marketplace at Lyft, is responsible for determining what inventory can be reliably offered for a given rider session and fulfilling rider requests. The group comprises several sub-teams that generate feasible offers for riders, match rider requests with drivers, and maintain a distributed state machine to track rides and drivers from request through completion.

We are seeking a Machine Learning Engineer to join the Fulfillment team and lead the design, development, and deployment of state-of-the-art machine learning systems. This role requires a strategic thinker who can balance high-level system architecture with hands-on technical implementation. You will collaborate across teams to shape the future of ride-sharing by leveraging machine learning and data science.

Responsibilities:

Design, build, and deploy machine learning models for real-time applications, including translating state-of-the-art research into production-ready solutions

Design and implement feature pipelines, model training workflows, and serving infrastructure using Lyft's ML platform

Evaluate ML system performance against business KPIs, run experiments, and drive continuous model improvement

Partner with ML engineers, product managers, data scientists, and software engineers to align ML initiatives with business goals

Leverage data-driven insights to inform and refine ML strategies and solutions

Write production-level code and participate in code reviews to ensure quality and share knowledge across the team

Experience:

BS/MS in Computer Science, or a related field

2+ years of experience in machine learning modeling or related fields

Experience with deep learning technologies for recommendation systems, including TensorFlow, PyTorch, or similar frameworks

Understanding of statistical concepts such as hypothesis testing, regression analysis, and performance evaluation metrics for machine learning

Experience with translating state-of-the-art ML research into production systems

Proficiency in Python, Golang, or other programming language

Proven ability to tackle ambiguous problems and deliver solutions at scale

Strong communication and interpersonal skills for effective cross-functional collaboration

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 New York City area is $140,800 - $176,000, 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.

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