Job description

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.

Requirements

Must-haves

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.
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
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

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

mlmachine learningpythongolang

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off

Parental Leave