AI Engineer, Kumo

• 3+ years of experience in machine learning, AI-driven product development, or software engineering
• Hands-on experience with vector databases, embeddings, and retrieval-augmented generation (RAG)
• Strong background in deep learning, transformers, and foundation models
• Experience with relational data, SQL, and structured reasoning
• Ability to work in a fast-paced startup environment, balancing research-driven innovation with product deployment

Desirable

• We are looking for a Senior Machine Learning Engineer to design and build AI-powered products that augment data scientists with intelligent agents
• You will work at the intersection of foundation models, agentic workflows, and user-centric AI systems, building tools and APIs that autonomously analyze data, suggest insights, and drive decision-making processes
• Building intelligent AI agents that interact with data scientists, understand their goals, and autonomously propose workflows for data analysis, prediction, and optimization
• Developing scalable AI products on top of Kumo’s relational model, integrating it with enterprise data systems
• Define and create seamless user experiences, ensuring that AI-generated insights are interpretable, actionable, and integrated into real-world business decisions
• Optimizing agentic workflows, allowing AI-driven systems to automate tasks, interact with APIs, and continuously refine their outputs based on user feedback
• Design, implement, and deploy AI-powered agents that assist data scientists in analyzing relational data and making more accurate decisions
• Develop user centricAPIs to build end-to-end user experiences and Foundation APIs as tools for the Agent
• Build adaptive, agentic workflows that interact with structured data, automate analytics, and propose next-best actions
• Work with foundation models for relational data, improving their ability to reason over complex business datasets
• Design human-in-the-loop interactions, allowing AI agents to collaborate effectively with data scientists, incorporate feedback, and refine workflows autonomously
• Work closely with ML researchers, engineers, and product teams to bring AI-driven solutions from research to production
• Optimize AI models for efficiency, interpretability, and real-world performance in enterprise environments