Senior ML Engineer — End-to-End, Remote-First

Coinbase; Richmond (VA); Remote; USD 214.000 - 225.000; Vollzeit

Zusammenfassung

Coinbase is seeking a Senior Machine Learning Engineer to own end-to-end ML initiatives across the lifecycle, from problem formulation and feature engineering to production serving and online experimentation. You will partner with Product, Data Science, and Engineering to scale multi-stage recommender and notification systems for millions of users.

The role emphasizes building AI-native workflows, responsible AI practices, and measurable business impact in a high-velocity environment.

Qualifikationen

• 5+ years of experience in machine learning engineering and software development.
• Proven track record in production ML systems that move metrics.
• Backend languages: Python or Go; proficiency with ML stacks (PyTorch, Spark, Kafka, etc.).
• Experience designing offline and online evaluation, and mentoring junior engineers.

Aufgaben

• Own end-to-end ML initiatives from problem formulation to production deployment and online A/B testing.
• Build multi-stage recommendation, retrieval, and contextual bandit models for personalized experiences.
• Design intelligent notification and targeting systems to improve user engagement.
• Develop production LLM pipelines and real-time market insights.
• Architect low-latency model serving and real-time feature pipelines, improving reliability.
• Mentor L3-L4 engineers and help shape the team roadmap.

Kenntnisse

ML engineering

Software development

Production ML

Mentoring

Tools

Python

Go

PyTorch

Spark/Databricks

Kafka

Ray

Tecton

Jobbeschreibung

Coinbase is seeking a Senior Machine Learning Engineer to own end-to-end ML initiatives across the lifecycle, from problem formulation and feature engineering to production serving and online experimentation. You will partner with Product, Data Science, and Engineering to scale multi-stage recommender and notification systems for millions of users.

The role emphasizes building AI-native workflows, responsible AI practices, and measurable business impact in a high-velocity environment.