ML Engineer — Build a Self-Learning Revenue Engine

Clay Labs; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Clay is seeking a Machine Learning Engineer to join the Learning Team and help build a self-learning revenue engine.

You'll ship intelligence features, design data pipelines, and contribute to ML platform and evaluation systems. You’ll collaborate with product teams to make surfaces smarter and impact users’ experience with Clay.

Ideal candidates have 5+ years in ML engineering, proficiency in production ML, and a track record of shipping ML features at scale in fast-moving startups.

Qualifications

• Minimum 5+ years in ML engineering or ML-heavy software engineering with production ML experience.
• Produce production-quality code and own reliable systems.
• Experience deploying LLMs in production and/or ML-based ranking/recommendations.
• Design data pipelines and feature infrastructure for data-intensive apps.
• Balance user experience with business impact in product-facing ML solutions.
• Comfort operating with ambiguity in fast-moving startups.

Responsibilities

• Build learning loops and net-new experiences integrated into the product.
• Develop the ML/data platform and data lake foundations for scalable ML.
• Create eval systems and monitoring to ensure quality and impact.
• Collaborate with product teams to make surfaces smarter across Clay.

Skills

5+ years ML experience

Production-grade code

LLMs in production

Data-intensive systems

Product sense

Ambiguity tolerance

AI space enthusiasm

Tools

Snowflake

dbt

Dagster

Job description

Clay is seeking a Machine Learning Engineer to join the Learning Team and help build a self-learning revenue engine.

You'll ship intelligence features, design data pipelines, and contribute to ML platform and evaluation systems. You’ll collaborate with product teams to make surfaces smarter and impact users’ experience with Clay.

Ideal candidates have 5+ years in ML engineering, proficiency in production ML, and a track record of shipping ML features at scale in fast-moving startups.