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.
Our engineering team is growing rapidly, and we are looking for a Machine Learning Engineer. As a machine learning engineer, you will be developing and launching the algorithms that power the platform's core services. Compared to similarly-sized technology companies, the set of problems that we tackle is incredibly diverse. They cut across transportation, economics, forecasting, mapping, personalization, and adaptive control. We are hiring motivated experts in each of these fields. We're looking for someone who is passionate about solving problems with data, building reliable ML systems, and is excited about working in a fast-paced, innovative, and collegial environment.
An ML SWE in the Integrity team is a specialized role focusing on the application of machine learning to enhance fraud detection and prevention. This role operates at a leadership and system ownership level comparable to a general SWE but with a deep specialization in ML. The individual will contribute significantly to the team's engineering excellence and operational responsibilities.
This role is a highly specialized engineering position that leverages deep machine learning expertise to directly impact the Integrity team's core mission: reducing fraud, ensuring trust and safety on the Lyft platform, and contributing to the development of cutting-edge AI-driven fraud-fighting platforms.
Responsibilities:
Core Responsibilities:
Develop & Lead ML Project Initiatives for Integrity, Identity and Pay: Partner with Engineers, Data Scientists, Product Managers, and Business Partners across the organization to apply machine learning for business and user impact, specifically in areas such as (supervised) fraud risk scoring, (unsupervised) anomaly detection and other applications. Drive the end-to-end lifecycle of ML projects within the Integrity domain.
Drive ML Engineering Excellence: Write production-quality code to deploy and scale machine learning models. Lead investments in architecture, observability, performance, platforms, shared libraries, and tools that support robust and efficient ML operations within the Integrity team.
Collaborate Cross-functionally on ML Solutions: Drive effective collaboration with cross-functional partners, including other engineering teams (e.g., Driver, Mapping, Security, Mobile Infra for signal integration), data scientists, product managers, and business partners, to define and implement comprehensive ML solutions for integrity challenges.
Mentor Junior Engineers in ML: Provide technical guidance and mentorship to junior engineers, support their onboarding processes, and actively participate in hiring efforts, particularly for candidates interested in machine learning and fraud prevention.
Specific ML-Focused Responsibilities & Experience (aligned with Integrity Roadmap):
Lead Core 2026 ML Initiatives for Risk Management:
Agentic AI & KarmaAI Workflows: This supports the "Agentic workflows to scale human workloads" and the broader Karma AI (kAI) goal of an end-to-end fraud fighting loop.
Risk Score & Model Lifecycle: Lead the retraining of the foundational fraud risk score model and the strategic sunsetting of legacy chargeback/debt models, ensuring adherence to RTB platform health standards for ML model lifecycle management.
Driver Fraud Model Development: Drive the development and deployment of the Location Spoofing ML model , including ingestion of map matching results and advanced feature sets.
Advance Foundational Signals:
Behavioral Fingerprinting: Investigate and build proof-of-concept models for Behavior Fingerprinting using client signals and sequence modeling, with an early use case in signup behavior to target card testing.
Data-Driven Problem Solving: Exhibit a strong passion for solving complex business problems using large-scale data, specifically focusing on identifying, analyzing, and mitigating diverse fraud vectors.
Drive ML Engineering Excellence (Enhance Platform RTB):
ML Infrastructure & MLOps: Demonstrate hands-on experience with robust ML infrastructure components, including 1-click deployment systems and auto-retraining pipelines. This supports the migration of ML pipelines to the latest MLP platforms and ensures high Platform Health for all maintained models.
Impact Evaluation: Rigorously evaluate the performance of deployed ML models against critical business metrics (PMM, fraud loss rate) to ensure direct alignment with Integrity's strategic objectives to Reduce fraud and abuse and Strengthen trust .
Experience:
B.S., M.S., or Ph.D. in Computer Science or other quantitative fields or related work experience
3+ years of Machine Learning experience
Passion for building impactful machine learning models leveraging expertise in one or multiple fields.
Proficiency in Python, Golang, or other programming language
Excellent communication skills and fluency in English
Strong understanding of Machine Learning methodologies, including supervised learning, forecasting, recommendation systems, reinforcement learning, and multi-armed bandits
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 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
Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program
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. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Range is not inclusive of potential equity offering, bonus or benefits. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.
