Senior Machine Learning Engineer

Coinbase

2d•$213,750 - $225,000•Remote

About The Position

The ML & Discovery team in Consumer Growth builds machine learning systems that help customers discover what to trade and engage with next across crypto, equities, derivatives, and prediction markets. Its systems power personalized feeds and asset ranking, intelligent notification orchestration, and LLM-driven market insights in the Coinbase app. As a Senior Machine Learning Engineer (L5), you will own initiatives across the machine learning lifecycle, from problem formulation and feature engineering through modeling, production serving, and online A/B experimentation. Working closely with Product, Data Science, and Engineering in a high-velocity, data-driven environment, you will shape the team roadmap, improve engineering quality, and use AI-native workflows to deliver measurable business impact.

Requirements

At least 5 years of experience in machine learning engineering and software development.
Track record of shipping production ML systems that improve customer and business metrics.
Proven experience in one or more relevant areas: recommendation systems, multi-stage ranking and retrieval, contextual bandits, notification targeting, or production LLM applications.
Strong software engineering and data skills in a backend language such as Python or Go.
Hands-on proficiency with modern machine learning and data stacks, such as PyTorch, GBDTs, Spark/Databricks, Ray, Kafka, and/or Tecton.
Practical knowledge of offline model evaluation, training-serving skew mitigation, and designing and interpreting online A/B experiments tied to business metrics.
Ability to drive complex projects end-to-end, make pragmatic architectural trade-offs, and mentor junior engineers.
Ability to use generative AI responsibly, maintain human oversight, and deliver business-ready outputs and measurable improvements in workflow efficiency, cost, and quality.

Responsibilities

Own end-to-end machine learning initiatives, from problem formulation and offline evaluation to production deployment and online A/B testing, driving measurable improvements in user engagement, retention, and trading activity.
Build and iterate on multi-stage recommendation, retrieval, and contextual bandit models for personalized experiences across crypto, equities, derivatives, and prediction markets.
Design notification orchestration, candidate ranking, and targeting systems that provide timely, high-value alerts while avoiding user fatigue.
Develop production LLM pipelines, grounding workflows, and automated evaluation loops for accurate, real-time market insights and personalized financial content.
Architect resilient, low-latency model-serving services and real-time feature pipelines; address technical debt, lead incident response, and improve operational reliability.
Partner with Product, Data Science, and Engineering to shape the team roadmap.
Mentor L3-L4 engineers and build AI-native workflows and automation to increase team velocity.