Clay
Machine Learning Engineer
About this role
Clay is seeking a Machine Learning Engineer to join its Learning Team and build intelligence features that enable the product to learn from user behavior and data. You'll design recommendation systems, develop ML infrastructure, and create evaluation frameworks that power Clay's self-learning revenue engine across product surfaces.
What you'll do
- Design and ship learning loop systems that leverage user behavior and business data to improve product experiences
- Build recommendation-first features from prototype through production deployment
- Develop ML and data platform infrastructure including data lakes and serving systems
- Create evaluation systems and online monitoring to ensure learning features are trustworthy and impactful
- Collaborate with data science, data platform, and product teams to align on data architecture and ML capabilities
- Evaluate and integrate new tools to accelerate the product vision
What they're looking for
- Machine learning system design and implementation
- Recommendation systems and ranking algorithms
- ML infrastructure and platform development
- Data pipeline and ETL architecture
- Model evaluation and monitoring frameworks
- Production ML deployment and serving
- Cross-functional collaboration and communication
- Python or similar ML-focused programming language
Benefits
- Access to world-class coaches specializing in creativity and management
- Work on AI at the core of a $5B valuation company
- Remote-friendly work environment
- Community equity offering opportunity
- Unique company culture with diverse team interests and backgrounds
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Clay
Clay builds go-to-market and developer experience infrastructure, including automation workflows, sales tools, and web experiences that help teams acquire customers and improve efficiency. The company is hiring GTM Engineers to build internal systems and automations, Web Developers to create conversion-focused experiences, and Software Engineers to improve developer tooling and AI integration.
View all jobs at ClayLikely interview questions
- Walk us through how you've built a learning loop or recommendation system in production—what were the key challenges and how did you measure success?
- Describe your experience building or scaling ML infrastructure—what data platforms or serving systems have you worked with?