AI Engineer - Relational Foundation Models & Agentic Systems
Location: Mountain View, CA
Department: Applied ML
Location Type: HYBRID
Employment Type: FULL_TIME
- Comfortable in an innovation pod or startup environment, moving quickly from idea → prototype → ship.
- A tinkerer at heart who’s built full‑stack apps (frontend, backend, data) and lately has been hands‑on with the LLM tooling ecosystem.
- Collaborative and easy to work with—you know how to partner with PMs/design/ML, bounce ideas, and get things done together.
- Bonus: experience as a Founding Engineer or early builder who has shaped product direction from the ground up.
About Kumo.ai
- Understand a user’s goal and autonomously propose workflows for analysis, prediction, and optimization.
- Interact with enterprise systems and APIs, orchestrating tools and data.
- Produce interpretable outputs that are easy to trust in real-world decisions.
- Demo prototypes and apps to customers, iterating on feedback and incorporating real use cases.
- You’ll work across product surfaces (UI/API), agent orchestration, data/infra, and model integration, collaborating tightly with product, design, ML research, and customers.
- 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).
- 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.
- Experience as a Founding Engineer or early builder at a startup/innovation pod.
- Experience with LangChain, LangGraph, LlamaIndex, OpenAI/Anthropic APIs, and multi-agent coordination libraries.
- Track record building full-stack features (you can dip into frontend/backend/data/infra as needed).
- Experience integrating agents with enterprise systems and APIs; designing foundation APIs for tools.
- Background in knowledge graphs, GNNs, causal inference, or structured reasoning with LLMs.
- MLOps and cloud (AWS/GCP), model/agent serving, prompt/runtime observability, and eval pipelines.
- Familiarity with guardrails, safety, and governance for enterprise AI.
- Builder mindset: you ship small, learn fast, and raise the bar with each iteration.
- Team-first: crisp communication, lightweight docs, and pairing with PM/design/ML to de-risk quickly.
- User-obsessed: you instrument everything, close the loop with customers, and let usage guide the roadmap.
- Pragmatic about research: you know when to use existing models vs. when to push the frontier.
- Ship a v1 agentic workflow powered by RFM that users adopt for a real analytics task (with instrumented evals & feedback).
- Demonstrate measurable improvements (accuracy/latency/cost/UX trust) via experiments and A/Bs.
- Land 1–2 integrations with enterprise data systems or tooling; document runbooks and guardrails.
- Deliver customer demos that inspire adoption and open new product opportunities.
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