Role Description 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. Demand for Predictive AI is accelerating faster than ever. Our customers include some of the world’s most influential enterprises across retail, e-commerce, consumer goods, fintech, travel, and technology. - Operate at true global scale - Hundreds of ML models - Billions of data points - Business-critical use cases across recommendations, forecasting, supply chain optimization, fraud, CRM, and more We are rapidly expanding our Applied Machine Learning team, a high-impact, highly technical group that sits at the center of our customer engagements. This team guides customers from their very first pilot all the way through to scaled, production-grade deployments of relational predictive models. This is a unique opportunity for someone who is: - Curious and intellectually hungry - Energized by startup culture - Motivated by high-growth environments - Excited to become an expert practitioner of cutting-edge AI models - Thrilled by the chance to work directly with Silicon Valley innovators, global brands, and leaders in data science and business What You’ll Do - 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, evals, 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, understanding 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. 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 people and navigating technical + 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. Requirements - 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, recsys, fraud, or other applied ML domains - Familiarity with GNNs, temporal models, or structured reasoning. - Enterprise integrations, data platforms, or productionizing ML 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. Benefits - Exposure to an extraordinary range of challenges and industries. - Learn faster due to diverse customer problems and datasets. - Support and eventually lead technical engagements with large, forward-thinking companies. - Build advanced predictive systems using GNNs, temporal models, forecasting engines, and next-generation workflows. - Work cross-functionally with engineering, ML research, product, and executive leaders. - Help define what enterprise ML looks like in practice.