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# Machine Learning Engineer, Fulfillment

- **Company:** Lyft Inc
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $140,800.0 - $176,000.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Code Review, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Regression Analysis, Recommender Systems, Tensorflow, Azure Machine Learning, Pytorch, Deep Learning, Information Technology, Golang, Programming Languages
- **Published:** October 22, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pthor5n3m1

## About the Role

* BS/MS in Computer Science, or a related field
 * 2+ years of experience in machine learning modeling or related fields
 * Experience with deep learning technologies for recommendation systems, including TensorFlow, PyTorch, or similar frameworks
 * Understanding of statistical concepts such as hypothesis testing, regression analysis, and performance evaluation metrics for machine learning
 * Experience with translating state-of-the-art ML research into production systems
 * Proficiency in Python, Golang, or other programming language
 * Proven ability to tackle ambiguous problems and deliver solutions at scale
 * Strong communication and interpersonal skills for effective cross-functional collaboration

## Description

* Design, build, and deploy machine learning models for real-time applications, including 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
 * Leverage 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