AI Engineer - Relational Foundation Models & Agentic Systems at Kumo

Mountain View, California, United States

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Not SpecifiedCompensation

Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, Machine Learning, Enterprise SoftwareIndustries

Requirements

Minimum Qualifications
1+ years in ML/AI product development or software engineering (startup or fast-paced product teams)
Hands-on with embeddings, vector databases, and RAG; practical experience evaluating retrieval quality
Strong background in deep learning/transformers/foundation models and LLM orchestration (tool use, planning, memory)
Experience with relational data & SQL; structured reasoning on business datasets
Proficiency in Python and familiarity with data wrangling (Pandas, NumPy)
Strong product sense and collaboration skills—comfortable working with PMs/design and iterating with users
Preferred Qualifications
Experience as a Founding Engineer or early builder at a startup/innovation pod
Experience with LangChain, LangGraph, LlamaIndex, OpenAI/Anthropic

Responsibilities

Design, implement, and deploy AI agents that assist data scientists on relational/SQL data and recommend next-best actions
Build user-centric APIs and product surfaces (web/UI or programmatic) that make agentic workflows feel seamless and reliable
Integrate Kumo’s Relational Foundation Model with enterprise data systems; contribute to tooling, retrieval, and guardrails
Develop adaptive, multi-step workflows (LLM orchestration, tool use, feedback loops) that continuously refine outputs
Ensure interpretability and evaluation: traceability of steps, confidence scoring, and human-in-the-loop review
Collaborate with PM/design/ML research to turn ambiguous problems into shippable product; instrument, measure, iterate
Demo your work to customers and community, serving as a visible builder and advocate for Kumo RFM
Optimize for latency, cost, and reliability in production environments (serving, caching, tracing, observability)

Skills

Key technologies and capabilities for this role

AI/MLLLMsSQLAPIsFull-Stack DevelopmentFrontendBackendData EngineeringAgentic WorkflowsRelational DataWorkflow Orchestration

Questions & Answers

Common questions about this position

What kind of experience makes a strong candidate for this AI Engineer role?

Ideal candidates are comfortable in innovation pod or startup environments, tinkerers who have built full-stack apps and worked hands-on with LLM tooling, and collaborative team players who partner with PMs, design, and ML teams effectively.

What are the key responsibilities of this AI Engineer position?

You'll design and deploy AI agents for relational/SQL data, build user-centric APIs and product surfaces, integrate the Relational Foundation Model with enterprise systems, develop adaptive workflows with LLM orchestration, and ensure interpretability through traceability and human-in-the-loop review.

Is this a remote or on-site position?

This information is not specified in the job description.

What is the salary or compensation for this role?

This information is not specified in the job description.

What is the company culture like at Kumo.ai?

Kumo.ai fosters a fast-paced, collaborative environment in innovation pods or startup settings, where you'll work closely with product, design, ML research teams, and customers to ship products quickly and shape direction from the ground up.

Kumo

Generates and deploys predictive models

About Kumo

Kumo.ai specializes in creating and implementing accurate predictive models for organizations that need reliable forecasts for critical operations. Their platform uses Graph Neural Networks to analyze raw relational data, which removes the need for manual data preparation and enhances prediction accuracy and efficiency. Unlike many competitors, Kumo.ai's platform streamlines the entire Machine Learning lifecycle, from data preparation to model deployment, while also optimizing costs by eliminating unnecessary infrastructure. The company aims to provide a quick return on investment for its clients, which range from small businesses to large enterprises, by offering flexible deployment options through Software as a Service (SaaS) and Private Cloud models. Kumo.ai is built by experienced professionals from top tech companies and has already gained the trust of leading organizations globally.

Mountain View, CaliforniaHeadquarters

2021Year Founded

$35.5MTotal Funding

SERIES_BCompany Stage

Fintech, AI & Machine LearningIndustries

51-200Employees

Benefits

Stock Options

Medical Insurance

Dental Insurance

Risks

Increased competition from Databricks' Marketplace may divert potential customers.

The rise of multimodal AI could overshadow Kumo's current offerings.

Rapid AI advancements by tech giants may set new industry standards Kumo must meet.

Differentiation

Kumo.AI uses Graph Neural Networks for predictive modeling, eliminating manual feature engineering.

The platform offers a SQL-like Predictive Querying Language for rapid AI model creation.

Kumo.AI integrates with Snowflake's Native App Framework, enhancing model performance and scalability.

Upsides

Kumo's $18M Series B funding will expand its platform and market reach.

Integration with Snowpark Container Services enhances deep learning capabilities within Snowflake Data Cloud.

Kumo's platform supports both SaaS and Private Cloud models, offering client flexibility.

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