Jobs
Benefits & Perks
•Healthcare
•Mental Health
•Parental Leave
•Pet Insurance
•401(k)
•Commuter Benefits
•Unlimited PTO
•Healthcare
•Mental Health
•Parental Leave
•Pet Insurance
•401k
•Commuter
•Unlimited Pto
Required Skills
Machine Learning
Python
GenAI
LLM fine-tuning
MLOps
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 over half a billion rides and counting, Lyft is solving hard problems at scale, leveraging AI and Machine Learning to better serve our customers. The Artificial Intelligence, Machine Learning, and Operations Research Platforms team (AIMLOR) is seeking a Senior Machine Learning Engineer to focus on building AI Platform components enabling critical AI applications across Lyft. Expertise with GenAI and platform building is a core requirement for this role. In this role, you will contribute to our platform which supports real-time, online, and offline AI and ML model execution, development, and iteration. You will work with a team of highly motivated Machine Learning and Software Engineers on challenging problems, defining solutions to directly impact systems across the entire business.
If you are interested in building an AI Platform at scale, with applications across each facet of the company, we are searching for you.
If you are a creative and critical thinker with experience in AI and machine learning systems, passionate about solving business problems using data and working in a dynamic, creative, and collaborative environment, we are searching for you.
Responsibilities:
-
Contribute to the roadmap and architecture based on technology and business needs
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Write well-crafted, well-tested, readable, maintainable code
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Have a good grasp and ability to explain the various tradeoffs made in decisions
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Participate in code reviews to ensure code quality and distribute knowledge
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Build features from tech specification to positive execution
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Incorporate considerations for business context and failure modes in your work
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Proactively participate in resolving ongoing incidents
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Unblock, support, effectively communicate, and obtain buy-in within your team to achieve results
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Share your knowledge by giving brown bags and tech talks
Experience:
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BSc/MSc in Computer Engineering, Computer Science, Machine Learning related field or relevant work experience
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5+ years of ML engineering experience working in any of these stacks: Python, GO, Java, etc.
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Experience with ML serving/training/deployment infrastructure; familiarity with cloud providers (e.g. AWS, Azure, Google Cloud); familiarity with GenAI ecosystem: LLMs, prompt engineering, MCP, RAG; hands-on experience with LLM fine-tuning techniques and frameworks (e.g. PEFT, LoRA); knowledge on deploying self-hosted LLMs (e.g. Llama, Mistral) for specialized tasks
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Experience with GenAI/LLM infrastructure, agent frameworks (e.g. Lang Chain, Lang Graph); MLOps tooling (MLflow, Airflow)
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Preferred: Experience with AI assisted coding such as Cursor or Claude Code
Benefits:
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Great medical, dental, and vision insurance options with additional programs available when enrolled
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Mental health benefits
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Family building benefits
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Child care and pet benefits
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401(k) plan with company match to help save for your future
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In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off
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18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible
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Subsidized commuter benefits
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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.
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About Lyft

Lyft
PublicLyft, Inc. is an American company offering ride-hailing services, motorized scooters, and bicycle-sharing systems in the United States and Canada, and, via its Free Now mobile app, Europe. Lyft is the second-largest ridesharing company in the United States after Uber.
1,001-5,000
Employees
San Francisco
Headquarters
$3.2B
Valuation
Reviews
2.7
10 reviews
Work Life Balance
3.8
Compensation
2.1
Culture
2.3
Career
2.0
Management
1.8
25%
Recommend to a Friend
Pros
Flexible scheduling and work-from-home options
Easy money and side hustle opportunities
Meet new people with good conversations
Cons
Unfair pay structure and low compensation
Easy deactivation based on rider complaints
Poor customer support for drivers
Salary Ranges
29 data points
Junior/L3
Mid/L4
Senior/L5
Junior/L3 · Analyst
1 reports
$146,004
total / year
Base
$126,960
Stock
-
Bonus
-
$146,004
$146,004
Interview Experience
5 interviews
Difficulty
4.0
/ 5
Duration
14-28 weeks
Offer Rate
100%
Experience
Positive 60%
Neutral 40%
Negative 0%
Interview Process
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Technical Interview/Take-home Challenge
5
System Design Interview
6
Onsite/Virtual Interviews
7
Offer
Common Questions
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Culture Fit
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