Machine Learning Engineer, Lyft Business & Ads
Build and deploy production machine learning systems across pricing, marketplace optimization, fraud detection, and agentic AI for Lyft Business. The role requires end-to-end ML ownership, experience with generative AI and LLM ecosystems, and the ability to independently scope high-impact projects.
About the job
Responsibilities
- Develop and deploy machine learning models across dynamic pricing, marketplace optimization, fraud detection, and anomaly or behavior detection in production environments serving millions of rides.
- Build and iterate on agentic AI systems, including LLM-powered analytical agents, to automate decision-making and reduce operational overhead.
- Design and implement feature pipelines, model training workflows, and serving infrastructure using Lyft's machine learning platform.
- Partner with Data Scientists on Algorithms and Decisions teams to move research prototypes from proof of concept to production at scale.
- Evaluate machine learning system performance against business KPIs, run experiments, and drive continuous model improvement.
- Identify and scope opportunities where machine learning can create leverage across Lyft Business verticals, including Healthcare, Lyft Pass, and Business Travel.
- Contribute to engineering standards for code quality, observability, documentation, and testing.
Requirements and Experience
- Experience with generative AI and LLM ecosystems, including prompt engineering, retrieval-augmented generation, agent frameworks such as LangChain or LangGraph, or fine-tuning.
- Exposure to graph-based machine learning methods, including graph neural networks, knowledge graphs, or network analysis.
- Experience with pricing, marketplace, or fraud-related machine learning problems.
- Familiarity with cloud machine learning services such as AWS SageMaker and Amazon Bedrock, or internal machine learning platforms.
- Track record of independently identifying and scoping machine learning projects.
Benefits and Compensation
- Extended health and dental coverage, life insurance, and disability benefits.
- Mental health, family-building, child-care, and pet benefits.
- Lyft-funded Health Care Savings Account.
- RRSP plan with company match.
- Flexible paid time off for salaried team members and paid time off for hourly team members.
- Eighteen weeks of paid parental leave for biological, adoptive, and foster parents.
- Subsidized commuter benefits and Lyft ride credits.
- Expected base pay range in the Toronto area: CAD $118,800–$148,500, excluding potential equity, bonus, and benefits.
- Hybrid schedule requiring at least three days per week in the office, including Mondays, Wednesdays, and Thursdays. Hybrid roles may be worked from anywhere for up to four weeks per year.
Skills
Machine Learning, Generative AI, LLMs, Prompt Engineering, Retrieval-Augmented Generation, LangChain, LangGraph, Graph Neural Networks, Knowledge Graphs, Aws Sagemaker, Amazon Bedrock, Feature Pipelines, Model Training, Model Serving, Python
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