Machine Learning Engineer
Lyft · New York, NY
About this role
**At Lyft — Machine Learning Engineer (Fulfillment)** Lyft’s purpose is to serve and connect. With a billion rides per year and counting, we solve hard problems in a rapidly growing domain using modern ML and petabyte-scale data. The **Fulfillment** group within the Marketplace is responsible for determining what inventory can be reliably offered for a given rider session and fulfilling rider requests. The team includes sub-teams that generate feasible offers, match rider requests with drivers, and maintain a distributed state machine to track rides and drivers from request through completion. We’re seeking a **Machine Learning Engineer** to lead the design, development, and deployment of state-of-the-art machine learning systems—balancing high-level architecture with hands-on implementation. You’ll 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, 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 - Use 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 related field) - 2+ years of experience in machine learning modeling (or related fields) - Experience with deep learning technologies for recommendation systems (e.g., TensorFlow, PyTorch, or similar) - Understanding of statistical concepts (hypothesis testing, regression analysis, and ML performance evaluation metrics) - Experience translating state-of-the-art ML research into production systems - Proficiency in Python, Golang, or other programming languages - Proven ability to tackle ambiguous problems and deliver solutions at scale - Strong communication and interpersonal skills
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