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Lyft

Senior Machine Learning Engineer, Recommendations

Reposted 28 Days Ago
In-Office
San Francisco, CA, USA
163K-204K Annually
Senior level
In-Office
San Francisco, CA, USA
163K-204K Annually
Senior level
Design, implement, and deploy machine learning models and systems for recommendations and related problems. Collaborate with engineers, data scientists, and product partners to analyze data, build proofs-of-concept, develop ML/optimization models, write production-quality code, and evaluate models against business goals.
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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, Marketplace, Growth, 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 peta-byte 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.

If you are a critical thinker with experience in machine learning workflows and LLMs, passionate about solving business problems using data and working in a dynamic, creative, and collaborative environment, we are searching for you.

We are seeking a Senior Machine Learning Engineer to join the Rider Applied AI team and lead the design, development, and deployment of state-of-the-art machine learning and artificial intelligence 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 AI, Machine learning and Data science.

Responsibilities:
  • Model Development & Research: Design, build, and deploy machine learning models for real-time applications, including translating state-of-the-art research into production-ready solutions.
  • System Design: Architect scalable, reliable ML pipelines that integrate seamlessly with existing backend systems.
  • Innovation & Applied Research: Stay ahead of the curve by exploring emerging algorithms, technologies (such as LLMs and LLM-based applications), and frameworks — critically evaluating new research and identifying high-impact use cases across business areas.
  • Collaboration: Partner with ML engineers, product managers, data scientists, and software engineers to align ML initiatives with business goals.
  • Data-Driven Decision Making: Leverage data-driven insights to inform and refine ML strategies and solutions.
  • Mentorship & Technical Leadership: Provide technical direction, mentor Junior engineers, and foster a culture of learning and collaboration.
  • Code Quality: Write production-level code and participate in code reviews to ensure quality and share knowledge across the team.
Experience:
    • M.S. or Ph.D. in Computer Science or related technical field
    • 5+ years (or Ph.D. with 3+ years) of experience in machine learning modelling 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 San Francisco area is $162,800 - $203,500, 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.

HQ

Lyft San Francisco, California, USA Office

185 Berry Street, San Francisco, CA, United States, 94107

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