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Clay is a $5B-valued SaaS platform that helps organizations execute growth through data enrichment and workflow automation. The company recently raised a $100M Series C and crossed $100M in revenue, backed by Sequoia, CapitalG, and First Round.
You'll join the Learning Team, a centralized group of MLEs and data scientists building the intelligence engine that powers learning loops across Clay's product. This is a greenfield opportunity: the team is new, its charter comes directly from company leadership, and learning loops are central to the product vision.
Key responsibilities:
- Design and ship systems that allow Clay to learn from user behavior and business data, building net-new recommendation-first experiences from prototype to production
- Build the ML and data platform infrastructure, including data lake foundations and serving infrastructure; evaluate new tools to accelerate the product vision
- Collaborate with data science and data platform teams on common data standards
- Create eval systems and online monitoring to ensure learning features are trustworthy and positively impact user experience
- Partner with product teams across the company to make their surfaces smarter
You'll own systems end-to-end, from architecture and design through shipping features that make Clay feel like it truly knows every customer. The role emphasizes pragmatic product sense, knowing when simple beats sophisticated, and optimizing for end-user experience and business impact.
Required: 5+ years in ML engineering or ML-heavy software engineering with shipped models and features in production; strong engineering fundamentals and production-quality code ownership; 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); comfort with ambiguity and passion for staying current on AI innovations.
Nice-to-haves: recommendation systems, search ranking, or personalization experience; eval framework design for LLM/ML systems; modern data stack familiarity (Snowflake, dbt, Dagster); fast-moving startup experience.
About Clay
SaaS / Enterprise Software; Sales — go-to-market data enrichment and workflow automation platform.
