ML Engineer / Data Scientist - Enterprise

Hilbert's AI•San Francisco, CA

4d•Onsite

About The Position

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 can sit across the table from enterprise customers — translating their business challenges into ML solutions and their results into strategic clarity. 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 our largest enterprise customers, where the stakes are high, the data is complex, and you are the person the customer trusts to explain what the models are doing and why it matters. 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 present a causal analysis to a room of VP-level stakeholders and make it land, 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 — with a particular focus on our largest enterprise accounts. You'll be hands-on every day — building models, designing pipelines, running experiments, interrogating data, and shipping to production. But you'll also be the technical face of Hilbert's ML capabilities to key customers: understanding their business context firsthand, shaping how we apply our systems to their problems, presenting results and recommendations, and building the trust that turns a vendor relationship into a strategic partnership. 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 — and you can bring the customer along on the journey. 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, informed by direct exposure to how different enterprises actually operate. 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 — and explain those choices to customers who want to understand what's possible with their data. 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. You'll be in the room when customers ask "so what do we do with this?" and the answer needs to be concrete. 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 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 need to hold this line with customers who may be pushing for faster, simpler answers.

Requirements

You're an ML engineer who ships to production. You write clean, testable Python. You care about how your models are served, monitored, and maintained — not just how they perform offline. Your work doesn't end at a notebook; it ends when the system is running and delivering value.
You're a product-minded ML engineer. You understand that a model with great offline metrics is useless if it doesn't move the customer's business. You frame every modeling decision in terms of the outcome it enables — and you push back when a technically elegant approach doesn't serve the actual problem.
You're a trusted technical partner to senior stakeholders. You've been in the room with VP- or C-level audiences and presented technical work in a way that earned trust and drove decisions. You're comfortable with hard questions, pushback, and the ambiguity of enterprise conversations. You don't oversell, you don't hide behind jargon, and you know how to make complexity accessible without dumbing it down.
You have strong B2C business knowledge. You understand the problems consumer businesses actually face — customer acquisition vs. retention economics, lifecycle dynamics, basket composition, churn drivers, promotional cannibalization, channel attribution, demand elasticity. This knowledge informs how you frame problems and design models — and it makes you credible with customers who live in this world every day.
You're a systems thinker. You understand how models, data flows, customer behavior, and business outcomes connect. You don't optimize one metric in a vacuum — you consider second-order effects and how your work fits the bigger picture.
You've built recommendation, search, and/or customer-based ML systems — collaborative filtering, content-based methods, ranking systems, segmentation, propensity modeling. You understand when each applies and why.
You know how to build for configurability. You've worked on or contributed to model architectures and pipelines that flex across multiple customers, segments, or contexts — not rigid, single-purpose implementations.
You create value from limited data. You make pragmatic modeling choices when data is sparse, noisy, or cold-start. You know when a simpler approach beats a complex one and aren't seduced by unnecessary sophistication.
You're rigorous about causality. You understand causal inference methods — difference-in-differences, instrumental variables, propensity scoring, synthetic controls — and know when to apply them. You design A/B tests properly and understand their limitations.
You communicate with clarity and conviction. You can present a causal analysis to a C-suite audience and make it land. You can write a one-pager that changes a decision. You explain your reasoning, not just your results. Communication is not a nice-to-have here — it's the job.
You take ownership. You don't wait for someone to define the problem perfectly. You dig in, frame it, propose an approach, and ship it. If something breaks or underperforms — or if a customer isn't getting value — you treat it as your problem.
You thrive in ambiguity. Problem definitions shift. Data availability surprises you. Customer priorities evolve. You're energized by figuring it out, not paralyzed by incomplete specs.
You move at startup speed. You understand what it means to be available, responsive, and biased toward action in a fast-moving, early-stage environment.

Nice To Haves

Experience in customer-facing data science or ML roles — solutions engineering, applied science, technical account management, or consulting where you owned the technical relationship
Experience with ML infrastructure — feature stores, model serving, orchestration, monitoring, or retraining pipelines
Experience with experimentation platforms and A/B testing infrastructure
Deep exposure to retail, e-commerce, CPG, or marketplace data environments and the business teams that operate in them
Experience at early-stage startups or high-growth companies where you wore multiple hats
Experience taking models from prototype to production deployment and owning them in production
Background in economics, econometrics, or quantitative social science that informs your causal thinking

Responsibilities

Build and deploy ML models and pipelines that power core product capabilities: recommendation systems, search relevance, customer segmentation, demand forecasting, and activation optimization
Contribute to configurable, multi-tenant model architectures that adapt across different customer contexts, data availability, and business requirements — not bespoke rebuilds for every use case
Own your models through to production — working with engineering on serving, monitoring, and reliability, not just handing off prototypes
Create meaningful models with the data that's actually available — not the data you wish you had. You extract signal from limited, noisy, or sparse datasets and reach for the right level of complexity
Design and run rigorous A/B tests — including understanding when A/B testing is insufficient and causal inference methods are required
Apply causal reasoning rigorously — you know the difference between correlation and causation, you surface true drivers, and you flag when others confuse the two
Think in systems. You don't build isolated models — you understand how recommendation, segmentation, scoring, and activation interact with each other and design your work to fit within the broader system
Move fast — prototype, validate, ship, iterate. You're comfortable with imperfect information and evolving requirements
Be the technical ML counterpart for our largest accounts — understanding their data, their business model, and their growth challenges firsthand
Present results, analyses, and recommendations to senior customer stakeholders with clarity, conviction, and appropriate nuance — you're the person they trust on the data science
Translate customer context into modeling decisions — what you learn in customer conversations directly informs how you build. You don't build in a vacuum; you build with deep knowledge of how the output will be used
Deliver analyses that drive customer decisions — not decks that get filed away. You connect model outputs to business outcomes and present them in a way that changes behavior
Hold the line on rigor — when customers want a simpler story than the data supports, you find a way to be honest and helpful at the same time. You build trust through intellectual integrity, not through telling people what they want to hear
Feed enterprise learnings back into the product — patterns you see across customers, gaps in our systems, new opportunities for the platform. Your customer exposure makes the whole team smarter