ML Engineer / Data Scientist - Enterprise

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Mental health support

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Relocation assistance

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source

Seniority

Mid From the posting source

Schedule

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Weekend work

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Deadline

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About this role

ML Engineer / Data Scientist - Enterprise San Francisco, California, United States ML Engineer - Data Scientist (Enterprise) 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

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