Lead ML Engineer / Data Scientist

About this role

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 ARCHITECTURES THAT GENERALIZE ACROSS FORTUNE 500 ENTERPRISES AND BELOVED CONSUMER BRANDS ALIKE.

We're looking for a Lead ML Engineer who thinks in systems, understands B2C business problems deeply, and can build the models and pipelines that power real growth outcomes — all with the ownership and urgency of a founder.

This is not a "build a model in a notebook and hand it off" role. You'll own the entire ML function — from problem framing through model development through production deployment through business impact — and you'll do it for enterprise customers where the stakes are real and the feedback loop is tight. If you understand why a recommender system matters to a retailer's P&L, can design a configurable ML system that works across customers without being rebuilt from scratch, and can explain causal impact to a room of executives 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, every pipeline, every system you build 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 across engineering, product, and GTM to define, build, and scale the ML systems at the heart of Hilbert. You'll be hands-on daily — building models, designing pipelines, interrogating data, and shipping to production — but you'll also set the scientific direction, establish rigor, and grow the team. B2C is our world. The problems we solve — demand prediction, customer lifecycle, personalization, activation — require someone who understands these domains deeply and can translate business context into model design 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 walk into on day one — and you'll be the one setting the strategy for how we solve them.

- Multi-tenant ML architectures that actually generalize — we serve enterprises with fundamentally different data shapes, catalog sizes, customer behaviors, and business constraints. The challenge is designing model architectures and pipelines that are configurable and adaptive across customers — not rebuilding bespoke systems for every account. You'll define the abstraction boundaries and decide what's shared versus customer-specific.

- 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 set the modeling philosophy for how we build reliable systems 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 hurdle is closing the loop between ML outputs and real commercial decisions — activation, merchandising, retention — in a way that's measurable and defensible. You'll own how models translate into impact, not just accuracy.

- Causal rigor in a world that wants quick answers — enterprise customers want to know why something is happening, not just what. The challenge is building causal inference into our systems and analyses in a way that's rigorous but practical — knowing when an A/B test is sufficient, when you need difference-in-differences or synthetic controls, and when the honest answer is "we can't know yet." You'll set the bar for analytical integrity.

WHAT YOU'LL DO:

Build — hands-on, every day

- Design, build, and deploy ML models and pipelines that power core product capabilities: recommendation systems, search relevance, customer segmentation, demand forecasting, and activation optimization

- Develop configurable, multi-tenant model architectures that adapt to different customer contexts, data availability, and business requirements without being rebuilt from scratch

- Engineer production-grade ML systems — not just prototypes. You own model serving, monitoring, retraining, and the infrastructure that keeps models reliable at scale

- Create meaningful models with the data that's actually available — not the data you wish you had. You know how to extract signal from limited, noisy, or sparse datasets

- Design and run rigorous A/B tests and experimentation frameworks — including understanding when A/B testing is insufficient and causal inference methods are required

- Deliver analyses that drive decisions — not dashboards that collect dust. You connect model outputs to business outcomes and communicate them with clarity

- Apply causal reasoning rigorously — you know the difference between correlation and causation, you design analyses that surface true drivers, and you flag when others confuse the two

Lead — set direction and raise the bar

- Define and own the ML roadmap in partnership with the founding team

- Think in systems. You don't build isolated models — you design interconnected systems where recommendation, segmentation, scoring, and activation reinforce each othe

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