Senior Machine Learning Engineer

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

• →Own end-to-end machine learning initiatives from problem formulation and offline evaluation to production deployment and online A/B testing, driving measurable lifts in user engagement, retention, and trading activity.
• →Build and iterate on multi-stage recommendation, retrieval, and contextual bandit models that serve personalized experiences to millions of users across crypto, equities, derivatives, and prediction markets.
• →Design intelligent notification orchestration, candidate ranking, and targeting systems that match users with timely, high-value alerts while avoiding fatigue and improving user experience.
• →Develop production LLM pipelines, grounding workflows, and automated evaluation loops that generate accurate, real-time market insights and personalized financial content.
• →Architect resilient, low-latency model serving services and real-time feature pipelines, eliminating technical debt, leading incident response, and raising our operational reliability bar.
• →Partner with Product, Data Science, and Engineering to shape the team roadmap, mentor L3-L4 engineers, and build AI-native workflows and automation that multiply team velocity.

• 5+ years of experience in machine learning engineering and software development, with a track record of shipping production ML systems that move customer and business metrics.
• Proven track record in one or more relevant domains: 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 like Python or Go, with hands-on proficiency in modern ML and data stacks (such as PyTorch, GBDTs, Spark/Databricks, Ray, Kafka, and/or Tecton).
• Deep practical knowledge of offline model evaluation, training-serving skew mitigation, and design/interpretation of online A/B experiments tied to business metrics.
• Demonstrated ability to drive complex projects end-to-end, make pragmatic architectural trade-offs, and mentor junior engineers.
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.

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