Machine Learning Engineer

Machine Learning Engineer @ Clay

Clay's ambition is to build a self-learning revenue engine: a product that gets smarter every time someone uses it. This means data, ML, and AI are at the heart of everything we are building. We're looking for a Machine Learning Engineer to join the Learning Team: a centralized group of MLEs and data scientists whose charter is building the intelligence engine that powers learning loops across every surface of the product.

You'll ship intelligence features at the heart of the product: systems that learn a customer's business from their data and behavior, ranking and recommendation experiences, net new 0 to 1 AI products, and the ML platform that makes all of it possible.

What You'll Do

• Build learning loops into the product
• Design and ship systems that allow Clay to learn and improve using user behavior and important business data. Build net-new recommendation-first experiences, from prototype through production.
• Build the ML and data platform

• Help stand up the infrastructure that underpins learning including data lake foundations and serving infrastructure.
• Evaluate new tools for their ability to accelerate our product vision.
• Collaborate with our data science and data platform teams to ensure we’re all using a common data language.

• Make quality measurable
• Build eval systems and online monitoring so learning features are trustworthy and ensure they are actually positively impacting users’ experience of Clay.
• Work across product teams
• The Learning Team maintains one shared roadmap serving all product teams; you'll partner with almost every product team at Clay to make their surfaces smarter.

What You'll Bring

• 5+ years in machine learning engineering or ML-heavy software engineering, with models and ML-powered 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 - you optimize for the end user experience and business impact, and know when simple beats sophisticated
• Comfort with ambiguity - much of this platform is being built from the ground up
• A passion for the AI space: you stay up-to-date on the latest innovations and tools, and are excited to be at the frontier

Nice to Haves

• Experience building recommendation systems, search ranking, or personalization
• Experience designing eval frameworks for LLM or ML systems
• Familiarity with modern data stack tools (Snowflake, dbt, Dagster) and data lake architectures
• Experience in fast-moving startup environments