Machine Learning Engineer

About Clay

AI is unleashing the biggest wave of company creation in history. Clay's mission is to be the engine those companies use to grow to their full potential.

Clay predicts the next best action for your business and then helps you take it. We started by aggregating the best data for B2B companies. Then, we built the infrastructure to run any personalized campaign on top of it, including emails, ads, landing pages, and workflows that make reps more productive. Now, we're building agents that can help grow your company for you. We're already helping thousands of customers — including Anthropic, OpenAI, Google, and Visa — go to market with unique data, signals, and AI research.

In 2026, we raised a $115M Series D at $7.1B Valuation led by Wellington — and are on track to cross $200M in revenue this quarter. We also launched a $1M Scholarship fund for GTME Education.

Some things to know about us:

• Our community includes 17,000+ customers, 200+ integration partners, 125+ agencies, 50+ Clay clubs, and 30k members on Slack.

• Our culture is unique inside and outside of work. Our team members are also DJs, activists, writers, clowns, marathoners, skydivers, psychedelic therapists, social workers, and more.

• All employees can work with world-class coaches who specialize in creativity, management, and more.

• Our operating principles — including negative maintenance and non-attached action — guide our work. Read more about them here.

• Read about us in the NYT, Forbes, First Round Review, and more.

Hear from our employees directly on our Glassdoor page!

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

Why Clay

This is a rare greenfield: the Learning Team is new, its charter comes straight from company leadership, and learning loops are central to Clay's product vision. You'll define the architecture, set the standards, collaborate on the product vision, and ship the features that make Clay feel like it truly knows every customer. We value ownership, clear thinking, and work that has real impact.