Lead ML Engineer / Data Scientist
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 other. You see how the pieces fit together and where leverage exists
Frame business problems as ML problems — and know when a simpler approach beats a complex model
Set engineering and scientific standards — validation methodology, experiment design, code quality, reproducibility, and deployment discipline
Prioritize across competing demands, keeping the team focused on highest-impact work
Communicate results, tradeoffs, and strategic recommendations clearly to founders, customers, and non-technical stakeholders
Be the tiebreaker on methodology and architecture — when the team debates approaches, you bring clarity
Grow — build the team and the culture
Hire, mentor, and develop ML engineers and data scientists as the team scales
Create an environment of scientific rigor without academic slowness — ship, validate, iterate
Build processes that work at startup speed — reviews and checkpoints that improve quality without killing velocity
Identify capability gaps and build the team to fill them
Lead by example: the team sees you in the data, in the code, in the hard problems — not just in planning docs
Who You Are
We care about how you think about problems, how you connect models to business impact, and how you make others around you sharper.
The profile:
You're an ML engineer who ships to production. You write clean, testable Python. You care about model serving, pipeline reliability, and monitoring — not just offline metrics. Your models don't live in notebooks; they run in production and you own them there.
You're a systems thinker. You don't optimize one metric in a vacuum — you understand how models, data flows, customer behavior, and business outcomes connect. You design for the system, not the silo.
You're a product-minded ML leader. You understand that a model with great offline metrics is useless if it doesn't move the customer's business. You frame every technical decision in terms of the outcome it enables — and you teach your team to do the same.
You have deep B2C business knowledge. You understand the problems that consumer businesses actually face — customer acquisition vs. retention economics, lifecycle dynamics, basket composition, churn drivers, promotional cannibalization, channel attribution, demand elasticity. You've lived in this world and it informs how you build.
You've built recommendation, search, and/or customer-based ML systems in production — not just in research. You understand collaborative filtering, content-based methods, ranking systems, segmentation, propensity modeling, and when each applies.
You build configurable systems, not one-off models. You've designed model architectures and pipelines that work across multiple customers, segments, or contexts with tunable parameters — not bespoke rebuilds for every use case.
You create value from limited data. You know how to make pragmatic modeling choices when data is sparse, noisy, or cold-start. You reach for the right level of complexity — not the most impressive one.
You're rigorous about causality. You understand causal inference methods — difference-in-differences, instrumental variables, propensity scoring, synthetic controls — and you apply them when correlation isn't enough. You design A/B tests properly and know 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. Communication is not a nice-to-have here — it's the job.
You take ownership at the team level. You don't just own your own models — you own the team's impact. If a pipeline breaks or a model underperforms, you treat it as your problem.
You thrive in ambiguity. Problem definitions shift. Data availability surprises you. You bring structure to chaos without killing speed — and you coach the team to operate the same way.
You move at startup speed and expect the same from your team. You understand what it means to be available, responsive, and biased toward action in a fast-moving, early-stage environment.
Strong pluses:
Experience with ML infrastructure at scale — feature stores, model serving, orchestration, monitoring, retraining pipelines
Experience with experimentation platforms and A/B testing infrastructure
Exposure to retail, e-commerce, CPG, or marketplace data environments
Prior experience as an ML lead, principal ML engineer, or founding ML/data science engineer at an early-stage or high-growth company
Track record of hiring and developing ML engineers and data scientists — not just managing them
Experience with real-time and batch ML serving patterns in production
You might be:
A senior ML engineer at a B2C company who's tired of optimizing one metric and wants to build the whole system. An ML lead at a startup who's ready for higher stakes and more ownership. Someone who came up through quantitative research or applied science and moved into ML engineering because they wanted to see models run in production, not just in papers. A founding ML engineer who built the function at their last company and wants to do it again where ML is the product. What matters: you think in systems, you understand the business, you ship models that work with real data in real production environments, and you communicate impact — not just methodology.
Location
San Francisco, US
Compensation
Competitive salary + equity reflecting the seniority and scope of the role. Compensation details and structure shared in next steps.
The Hiring Journey
Short form → Intro call → Practical working session → Team conversations → Offer
Fast, human, no bureaucracy.