Machine Learning Engineer
Claylabs · San Francisco
About this role
## About Clay AI Clay is building a self-learning revenue engine—an AI product that gets smarter every time someone uses it. Clay helps businesses predict the next best action and take it, powering personalized campaigns (emails, ads, landing pages, and workflows) and new agent-based growth capabilities. ## Role: Machine Learning Engineer (Learning Team) Join Clay’s Learning Team, a centralized group of MLEs and data scientists building the intelligence engine that powers learning loops across the product. You’ll ship ML intelligence features including: - Systems that learn a customer’s business from their data and behavior - Ranking and recommendation experiences - Net-new 0→1 AI products - The ML platform enabling all of the above ## What You’ll Do - Build learning loops into the product - Design and ship systems that learn and improve from user behavior and key business data - Create recommendation-first experiences from prototype to production - Build the ML and data platform (data lake foundations + serving infrastructure) - Evaluate new tools to accelerate Clay’s product vision - Collaborate with data science and data platform teams to align on a common data language - Make quality measurable with eval systems and online monitoring - Partner across product teams to make Clay surfaces smarter ## What You’ll Bring - 5+ years in machine learning engineering or ML-heavy software engineering (models/ML features shipped to production) - Strong engineering fundamentals; you write production-quality code and own systems - Experience with LLMs in production (prompting, evals, guardrails, fine-tuning) and/or classical ML (ranking, recommendations, propensity models) - Experience building data-intensive systems (pipelines, feature infrastructure, retrieval, serving) - Pragmatic product sense—optimize for end-user experience and business impact - Comfort with ambiguity (platform built from the ground up) - Passion for AI and staying current with new innovations ## Nice to Haves - Experience building recommendation systems, search ranking, or personalization - Experience designing eval frameworks for LLM/ML systems - Familiarity with modern data stack tools (Snowflake, dbt, Dagster) and data lake architectures - Experience in fast-moving startup environments ## Why Clay This is a rare greenfield opportunity. The Learning Team is new, chartered directly by company leadership, and learning loops are central to Clay’s product vision. You’ll help define architecture, set standards, collaborate on the product vision, and ship features that make Clay feel like it truly knows every customer.
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