AI Engineer

About Kumo

Kumo is building the next generation of AI for structured data. With our Relational Foundation Model (RFM), we help some of the world’s largest companies transform their data into predictions, decisions, and end‑to‑end automated systems. Our culture is collaborative, fast‑moving, and deeply user‑obsessed. We value people who take initiative, learn quickly, communicate clearly, and enjoy solving hard technical + people challenges.

Why This Role (and Why Now)

• Demand for Predictive AI is accelerating faster than ever.
• Our customers include influential enterprises across retail, e‑commerce, consumer goods, fintech, travel, and technology.
• These organizations operate at global scale, with hundreds of ML models, billions of data points, and business‑critical use cases such as recommendations, forecasting, supply chain optimization, fraud, CRM, and more.
• The Applied Machine Learning team guides customers from pilot to scaled, production‑grade deployments of relational predictive models.

Responsibilities

• Support and eventually own technical success for enterprise customers adopting the Kumo platform.
• Design and build prototypes, workflows, and models across use cases such as:
  • Recommendations & personalization
  • Forecasting & demand planning
  • Fraud detection & risk modeling
  • Supply chain & logistics optimization
  • Banking & financial analytics
  • CRM/growth marketing & user modeling
• Work hands‑on with large‑scale relational datasets, customer pipelines, and production ML systems.
• Guide customers through modeling choices, data structures, evaluations, trust, interpretability, and rollout plans.
• Translate ambiguous customer needs into concrete ML solutions and RFM workflows.
• Collaborate closely with Kumo engineering and research teams to improve platform capabilities.
• Act as a technical leader and trusted advisor, recognizing that deploying ML is as much a people and business challenge as it is a technical one.
• Deliver demos, workshops, best practices, and partner with executives, PMs, analysts, and data scientists.

Minimum Qualifications

• Bachelor’s or Master’s in a STEM field (CS, EE, Math, Physics, Stats, etc.).
• Strong fundamentals in data science, statistics, or machine learning coursework.
• Real‑world experience via internships, research, industry work, or substantial project work.
• Demonstrated intellectual curiosity and initiative (personal ML/AI projects, open source, research, hackathons, or other hands‑on experience).
• Strong communication skills; comfortable working with both technical and non‑technical audiences.
• Genuine enthusiasm for ML/AI, modern modeling approaches, and applying them to real business problems.
• Motivated, self‑driven, excited to learn fast, and comfortable in a high‑velocity startup environment.

Preferred Qualifications (Bring Strength in at Least One Area)

• Deeper expertise in one or more of:
  • ML infrastructure / data engineering
  • Full‑stack development for ML apps
  • LLM orchestration, agent systems, or model tuning
  • Large‑scale distributed systems
  • Forecasting, recommender systems, fraud, or other applied ML domains
  • Familiarity with GNNs, temporal models, or structured reasoning
  • Enterprise integrations, data platforms, or productionizing ML

We do not expect candidates to have all of these. Deep strength in one area + strong Data Science fundamentals is ideal.

Working Model

• Hybrid: 1+ in‑person days per week with teammates located in Chapel Hill, Raleigh, Durham, Cary, and RTP.
• Onboarding: 1–2 weeks in person at our SF Bay Area HQ.
• Start dates:
  • Full‑time starting January or onwards (open to early graduates).
  • Part‑time (30 hrs/week) available immediately with option to convert to full time after graduation.

Success Looks Like (First 3–6 Months)

• Support and eventually lead multiple major customer engagements, delivering real business impact.
• Solve multiple challenging predictive machine learning problems by applying data science skills to large‑scale datasets.
• Build prototypes and workflows using RFM that demonstrate value and drive adoption.
• Collaborate with engineering to improve reliability, performance, and model quality across use cases.
• Earn trust from customer technical teams and become their go‑to person for ML strategy and execution.

Why Join Kumo? (Benefits)

• Exposure to an extraordinary range of challenges and industries, often seen only after many years in the field.
• Rapid learning environment: every customer brings a new problem, dataset, constraints, and opportunity to push model frontiers.
• Opportunity to support and eventually lead technical engagements with some of the largest and most forward‑thinking companies in the world.
• Build advanced predictive systems using GNNs, temporal models, forecasting engines, and next‑generation agentic workflows.
• Work cross‑functionally with engineering, ML research, product, and executive leaders, both internally and at the customer.
• Help define what enterprise ML looks like in practice: the tools, processes, workflows, and impact.

You’ll thrive in this role if you bring strong ML fundamentals, excellent instincts with people, and a drive to push yourself—and the technology—further than ever before.