At Lyft, our purpose is to serve and connect. To do this, we start with our own community by creating an open, inclusive, and diverse organization.
Machine Learning is at the heart of our products and decision-making. We’re looking for passionate, driven, engineers to build systems that empower ML models to make our products more predictive, personalized, and adaptive. 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.
As a machine learning software engineer, you will be developing production machine learning models that provide high quality support experiences for both riders and drivers. You will be working on a wide array of challenges ranging from large scale distributed model training, large language model development, automating machine learning model lifecycle, implementing model monitoring, enabling reinforcement learning and much more. You will work with modelers across the company and build infrastructure to incorporate the rapid developing needs in various domains.

EXPERIENCE:

• B.S., M.S., or Ph.D. in Computer Science and experience in distributed systems and machine learning
• 3+ years of Machine Learning experience
• Passion for building scalable and extensible solutions for machine learning development and productionisation towards short term and long term business and user impact
• Proficiency in Python, Golang, or other programming language
• Excellent communication skills
• Strong understanding of Machine Learning fundamentals, including supervised learning, large language models, forecasting, recommendation systems and reinforcement learning
• Bonus Qualifications: Experience with deep learning frameworks and LLM workflows + prompt engineering is a plus.

• Partner with Machine Learning Engineers, Data Scientists, Software Engineers and Product Managers to develop advanced systems for business and user impact
• Collaborate with cross-functional teams to understand requirements and translate them into technical solutions
• Evaluate when to build and when to reuse existing components including open source solutions
• Continuously research and stay updated with the latest advancements in machine learning and natural language processing
• Participate in code reviews, provide constructive feedback, and uphold best practices in ML engineering
• Create and maintain comprehensive documentation for models, experiments, and processes
• Write production quality code that scales with use
• Help establish roadmap and architecture based on technology and our needs