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.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.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.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, but you'll also set the scientific direction, establish rigor, and grow the team.Our Current HurdlesMulti-tenant ML architectures that actually generalize — design model architectures and pipelines that are configurable and adaptive across customers.Extracting real signal from messy, limited data — enterprise data is never clean and rarely complete; set the modeling philosophy for reliable systems.Connecting model outputs to business actions — close the loop between ML outputs and real commercial decisions; own how models translate into impact.Causal rigor in a world that wants quick answers — build causal inference into systems and analyses that are rigorous but practical.What you'll do:Build — hands-on, every dayDesign, build, and deploy ML models and pipelines that power core product capabilities: recommendation systems, search relevance, customer segmentation, demand forecasting, and activation optimizationDevelop configurable, multi-tenant model architectures that adapt to different customer contexts, data availability, and business requirements without being rebuilt from scratchEngineer production-grade ML systems — not just prototypes. You own model serving, monitoring, retraining, and the infrastructure that keeps models reliable at scaleCreate meaningful models with the data that's actually available — extract signal from limited, noisy, or sparse datasetsDesign and run rigorous A/B tests and experimentation frameworks — including understanding when A/B testing is insufficient and causal inference methods are requiredDeliver analyses that drive decisions — connect model outputs to business outcomes and communicate them with clarityApply causal reasoning rigorously — difference between correlation and causation; flag when others confuse the twoLead — set direction and raise the barDefine and own the ML roadmap in partnership with the founding teamThink in systems. Design interconnected systems where recommendation, segmentation, scoring, and activation reinforce each otherFrame business problems as ML problems — know when a simpler approach beats a complex modelSet engineering and scientific standards — validation methodology, experiment design, code quality, reproducibility, and deployment disciplinePrioritize across competing demands, keeping the team focused on highest-impact workCommunicate results, tradeoffs, and strategic recommendations clearly to founders, customers, and non-technical stakeholdersBe the tiebreaker on methodology and architecture — bring clarity when the team debates approachesGrow — build the team and the cultureHire, mentor, and develop ML engineers and data scientists as the team scalesCreate an environment of scientific rigor without academic slowness — ship, validate, iterateBuild processes that work at startup speed — reviews and checkpoints that improve quality without killing velocityIdentify capability gaps and build the team to fill themLead by example: the team sees you in the data, in the code, in the hard problems — not just in planning docsWho You AreWe 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. Write clean, testable Python; care about model serving, pipeline reliability, and monitoringYou're a systems thinker. Design for the system, not the siloYou're a product-minded ML leader. Frame technical decisions by the outcome they enableYou have deep B2C business knowledge. Customer acquisition vs. retention economics, lifecycle dynamics, basket composition, churn drivers, promotional cannibalization, channel attribution, demand elasticityYou've built recommendation, search, and/or customer-based ML systems in productionYou build configurable systems, not one-off models.You create value from limited data. Pragmatic modeling choices under sparsity, noise, or cold-startYou're rigorous about causality. Causal inference methods and proper A/B test designYou communicate with clarity and conviction. Present causal analysis to C-suite; write a one-pager that changes a decisionYou take ownership at the team level.You thrive in ambiguity.You move at startup speed and expect the same from your team.Strong pluses:Experience with ML infrastructure at scale — feature stores, model serving, orchestration, monitoring, retraining pipelinesExperience with experimentation platforms and A/B testing infrastructureExposure to retail, e-commerce, CPG, or marketplace data environmentsPrior experience as an ML lead, principal ML engineer, or founding ML/data science engineer at an early-stage or high-growth companyTrack record of hiring and developing ML engineers and data scientists — not just managing themExperience with real-time and batch ML serving patterns in productionLocationSan Francisco, USCompensationCompetitive salary + equity reflecting the seniority and scope of the role. Compensation details and structure shared in next steps.The Hiring JourneyShort form → Intro call → Practical working session → Team conversations → OfferFast, human, no bureaucracy.
about 2 months ago