ML Engineer / Data Scientist - Core

HILBERT IS BUILDING THE ML SYSTEMS THAT POWER DEMAND INTELLIGENCE FOR THE WORLD'S LARGEST CONSUMER COMPANIES — RECOMMENDATION ENGINES, DEMAND FORECASTING, CUSTOMER LIFECYCLE MODELS, AND ACTIVATION SYSTEMS THAT MUST WORK ACROSS WILDLY DIFFERENT RETAILERS, DATA ENVIRONMENTS, AND BUSINESS CONTEXTS. THIS ISN'T SINGLE-TENANT MODEL BUILDING; IT'S DESIGNING CONFIGURABLE, PRODUCTION-GRADE ML SYSTEMS THAT GENERALIZE ACROSS FORTUNE 500 ENTERPRISES AND BELOVED CONSUMER BRANDS ALIKE. We're looking for an ML Engineer who understands B2C business problems deeply, builds models and pipelines that work with real-world data, and delivers systems that drive real growth outcomes — all with the ownership and urgency of a startup. This is not a "receive a ticket, train a model, hand off a notebook" role. You'll own problems end-to-end — from framing through modeling through production deployment through impact — for enterprise customers where the stakes are real and the feedback loop is tight. If you understand why churn analysis matters differently for a grocery retailer versus a fashion marketplace, can build a recommendation system that works with sparse data and runs reliably in production, and can walk a customer through your causal analysis with clarity and conviction, we want to meet you. WHY HILBERT AI Hilbert is building the demand intelligence platform used by world-class B2C leaders — including the world's largest retailer — to unlock compounding growth outcomes. We sit at the intersection of AI, data, and commercial activation for retail and e-commerce. We're scaling fast with top-tier investors behind us. ML systems are the engine behind what we deliver to customers — which means every model you build, every pipeline you ship, every system you contribute to has direct, measurable impact on enterprise revenue. We're a small, talent-dense, low-ego team. We value ownership, speed, intellectual honesty, and shipping real impact. THE ROLE You'll work directly with the founding team and alongside engineering, product, and GTM to build and improve the ML systems at the heart of Hilbert. You'll be hands-on every day — building models, designing pipelines, running experiments, interrogating data, and shipping to production. B2C is our world. The problems we solve — demand prediction, customer lifecycle, personalization, activation — require someone who understands these domains and can translate business context into modeling and engineering decisions. The environment is high-autonomy and high-ambiguity. Data is often messy, incomplete, or limited. You thrive in exactly those conditions. OUR CURRENT HURDLES These are the kinds of problems you'll be working on from day one. - Multi-tenant ML systems that actually generalize — we serve enterprises with fundamentally different data shapes, catalog sizes, customer behaviors, and business constraints. The challenge is contributing to model architectures and pipelines that are configurable and adaptive across customers — not rebuilding bespoke systems for every account. You'll work on the abstractions that make this possible. - Extracting real signal from messy, limited data — enterprise data is never clean and rarely complete. Cold-start problems, sparse interaction histories, inconsistent taxonomies, missing features — this is the norm, not the exception. You'll need to make pragmatic modeling choices that produce real value when the data fights back. - Connecting model outputs to business actions — a recommendation score or a demand forecast is worthless if it doesn't change what an operator actually does. The challenge is closing the loop between ML outputs and real commercial decisions — activation, merchandising, retention — in a way that's measurable and defensible. - Causal rigor in a world that wants quick answers — enterprise customers want to know why something is happening, not just what. The challenge is applying causal inference in a way that's rigorous but practical — knowing when an