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Senior Machine Learning Engineer – Foundation Models & AI Agents for Relational Data

About Kumo.ai


Kumo.ai is redefining enterprise AI with foundation models for relational data, enabling businesses to make more accurate decisions through AI-powered agents. Our mission is to build intelligent systems that help organizations predict, optimize, and act autonomously—transforming how data scientists and analysts work with structured data.


We are developing agentic AI workflows that interact with data scientists, streamline data-driven decision-making, and autonomously propose, refine, and execute complex predictive workflows. Our technology bridges the gap between foundation models and real-world enterprise applications, making AI a trusted, proactive assistant for data teams.


The Role


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 that autonomously analyze data, suggest insights, and drive decision-making processes.


Your work will focus on:

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 foundation model, integrating it with enterprise data systems.

Creating 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.


Key Responsibilities
  • Design, implement, and deploy AI-powered agents that assist data scientists in analyzing relational data and making more accurate decisions.
  • 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.
  • Develop developer-friendly APIs and tools to integrate Kumo’s AI models into enterprise analytics platforms.
  • 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.


Minimum Qualifications
  • 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.
  • Proficiency in Python, PyTorch, TensorFlow, or JAX for machine learning development.
  • Experience with relational data, SQL, and structured reasoning.
  • Ability to work in a fast-paced startup environment, balancing research-driven innovation with product deployment.


Preferred Qualifications
  • Experience in building AI agents that interact with enterprise systems and APIs.
  • Background in causal inference, knowledge graphs, or structured reasoning with LLMs.
  • Experience in building AI-powered analytics tools for data scientists or business analysts.
  • Experience with MLOps, cloud-based AI infrastructure (AWS/GCP), and model serving frameworks.
  • Strong understanding of user-centric AI and human-computer interaction principles.


Why Join Us?


Shape the future of AI-driven decision-making by developing intelligent AI agents that work alongside data scientists.

Work on cutting-edge relational foundation models and AI-powered enterprise products.

Collaborate with top AI researchers, engineers, and product leaders in a high-impact environment.

Competitive salary, equity, and benefits package.

A culture that values innovation, collaboration, and technical excellence.

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What You Should Know About Senior Machine Learning Engineer – Foundation Models & AI Agents for Relational Data, KUMO

At Kumo.ai, we are on a mission to revolutionize enterprise AI, and we are thrilled to invite a Senior Machine Learning Engineer to join our team in Mountain View, CA. This is an exciting opportunity to design and build AI-powered products that will assist data scientists in their quest for insightful data analysis. In this role, you will bring your expertise in foundation models and AI agents to create intelligent systems that autonomously analyze relational data and propose actionable insights. You will be at the forefront of developing technologies that empower organizations to make informed decisions efficiently. Imagine crafting adaptive workflows that can intuitively interact with structured datasets, streamlining how data teams work with AI. Collaboration will be key as you work alongside ML researchers and engineers to integrate advanced AI solutions into real-world applications. Your hands-on experience with Python, deep learning frameworks like PyTorch or TensorFlow, and vector databases will be essential in driving the development of innovative tools. If you are passionate about shaping the future of data-driven decision-making and creating user-friendly AI interactions, Kumo.ai is the perfect place for you. Join us and be a part of a culture that values innovation, collaboration, and excellence in technology, all while enjoying a competitive salary and benefits package. Let’s transform the world of data together!

Frequently Asked Questions (FAQs) for Senior Machine Learning Engineer – Foundation Models & AI Agents for Relational Data Role at KUMO
What are the main responsibilities of a Senior Machine Learning Engineer at Kumo.ai?

As a Senior Machine Learning Engineer at Kumo.ai, your primary responsibilities will involve designing and building AI-powered agents that assist data scientists in analyzing relational data. You will also focus on creating adaptive workflows that automate analytics and suggest next-best actions.

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What qualifications are required for the Senior Machine Learning Engineer position at Kumo.ai?

To qualify for the Senior Machine Learning Engineer role at Kumo.ai, candidates should have at least 3 years of experience in machine learning or AI product development, proficiency in Python and various machine learning frameworks, and hands-on experience with relational data, SQL, and vector databases.

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How does Kumo.ai support the development of AI products for data scientists?

Kumo.ai supports the development of AI products by focusing on creating intelligent systems that understand data scientists’ goals and autonomously propose workflows for data analysis, thereby enhancing the productivity and efficacy of data-driven decision-making.

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What technologies will I work with as a Senior Machine Learning Engineer at Kumo.ai?

As a Senior Machine Learning Engineer at Kumo.ai, you will work with cutting-edge technologies related to foundation models, deep learning, vector databases, and APIs. Familiarity with tools such as PyTorch and TensorFlow will also be vital for your role.

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What is the company culture like at Kumo.ai for the Senior Machine Learning Engineer?

Kumo.ai fosters a culture that values innovation, collaboration, and technical excellence. As a Senior Machine Learning Engineer, you will collaborate with top-tier AI researchers and product leaders in a high-impact environment that encourages creativity and forward-thinking.

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Common Interview Questions for Senior Machine Learning Engineer – Foundation Models & AI Agents for Relational Data
What experience do you have with deep learning frameworks relevant to the Senior Machine Learning Engineer role?

Discuss your hands-on experience with frameworks like PyTorch, TensorFlow, or JAX and how you have used them to develop machine learning models in past projects.

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Can you explain what foundation models are and their significance in AI?

Foundation models are pre-trained models that can be fine-tuned for specific tasks. They significantly improve efficiency in building AI applications by providing a strong starting point, particularly in handling complex data.

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How do you approach optimizing AI models for efficiency and interpretability?

Explain your strategies for improving model performance, such as feature selection, hyperparameter tuning, and refining model architectures to enhance interpretability while maintaining efficiency.

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Describe a challenging project you worked on involving relational data.

Share a specific example of a project that involved complex relational data, detailing your approach to the problem, tools used, and outcomes achieved.

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What methods do you use to ensure seamless human-in-the-loop interactions in AI systems?

Discuss techniques like feedback loops and user experience design principles that help create intuitive interactions between AI agents and users.

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How do you stay up-to-date with the latest trends in AI and machine learning?

Mention any relevant journals, conferences, online courses, or communities you follow to keep yourself informed about the rapidly evolving AI landscape.

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What is your understanding of MLOps and its importance in deploying AI solutions?

Explain MLOps as the practice of managing ML lifecycle processes, emphasizing its importance in streamlining deployment, monitoring, and maintaining AI solutions in production.

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Can you describe your experience with implementing scalable AI solutions?

Provide examples of how you have designed and implemented scalable AI applications, focusing on architecture decisions, cloud solutions, or specific frameworks you utilized.

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What challenges do you foresee in integrating AI models with enterprise systems?

Discuss potential issues such as data quality, compatibility of APIs, and user adoption, along with strategies for addressing these challenges.

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Why do you believe user-centric AI is crucial for the role of a Senior Machine Learning Engineer?

Emphasize that user-centric AI focuses on creating systems that prioritize the needs of end-users, ultimately leading to better engagement, adoption, and value delivery in AI products.

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Full-time, on-site
DATE POSTED
March 14, 2025

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