Real-Time ML Engineer, Fulfillment Platform (Hybrid)

Lyft Inc; New York (NY); Hybrid; USD 141,000 - 176,000; Full time

Benefits offered by this job

Healthcare benefits

Mental health benefits

Family building benefits

Child care benefits

401(k) with company match

Paid time off

Job summary

Lyft Inc. seeks a Machine Learning Engineer to join the Fulfillment team, designing and deploying state-of-the-art ML systems that power real-time rider offers and matching between riders and drivers.

You will balance system architecture with hands-on implementation, collaborating across ML, data science, product, and software teams. You will translate cutting-edge research into production, build feature pipelines and serving infra, and evaluate ML performance against business KPIs to drive

Qualifications

• BS/MS in Computer Science or related field.
• 2+ years of ML modeling experience.
• Experience with deep learning for recommendation systems (TensorFlow/PyTorch).
• Understanding of statistical concepts and model evaluation metrics.
• Experience translating ML research into production systems.
• Proficiency in Python, Go, or similar languages.
• Ability to tackle ambiguous problems and deliver scalable solutions.
• Strong cross-functional communication.

Responsibilities

• Design, build, and deploy ML models for real-time applications.
• Create feature pipelines, training workflows, and serving infrastructure.
• Evaluate model performance against business KPIs and iterate.
• Collaborate with ML engineers, PMs, data scientists, and software engineers.
• Use data-driven insights to refine ML strategies.
• Write production-grade code and participate in code reviews.

Skills

Machine learning experience

Deep learning

Statistics concepts

Production ML deployment

Python

Go

Problem solving

Communication

Education

BS/MS in CS or related field

Tools

TensorFlow

PyTorch

Job description

Lyft Inc. seeks a Machine Learning Engineer to join the Fulfillment team, designing and deploying state-of-the-art ML systems that power real-time rider offers and matching between riders and drivers.

You will balance system architecture with hands-on implementation, collaborating across ML, data science, product, and software teams. You will translate cutting-edge research into production, build feature pipelines and serving infra, and evaluate ML performance against business KPIs to drive