Does this employer sponsor visas?
Source: US DOL Office of Foreign Labor Certification, LCA disclosure data, FY2026 Q2. Government data is published roughly one quarter in arrears.
This advert was matched to Software Engineer, a job title LYFT, Inc. has itself sponsored.
The role, as LYFT, Inc. describes it
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, gen
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