Member of Technical Staff - Applied Science
reservoirMillbrae (CA)
Member of Technical Staff - Applied Science
Posted 10 days ago
reservoirMillbrae (CA)
SENIORITY
Mid
About the role
About Reservoir Reservoir builds the intelligence layer for hospitality. Our mission is to help operators understand their customers and turn that understanding into better service and business decisions. We connect fragmented hotel data into a unified view of guest journeys and operations, then apply machine learning and AI to it, with secure data processing at the core. Many of the problems we work on don’t have standard solutions yet. We’re an early-stage team that combines product development, engineering and applied research. Each of us owns problems end to end, from defining them to measuring the results in production.
The role:
You’ll own the models at the core of Reservoir: matching records that belong to the same guest, predicting what guests want and estimating the effect of operational decisions. Together with product and engineering, you’ll decide which problems matter, then take each one from raw data to tested software in production. Much of this work runs on personal data that must stay isolated per customer, so you’ll also develop methods to detect and mask it and measure what masking costs in model quality. NLP, deep learning and LLMs are part of the toolkit where they fit. The focus will move as we learn from customers.
Who you are:
You’ve done applied research and shipped statistical or machine learning systems, and you can show how you tested them. You design experiments against clear baselines and know where they go wrong: leakage, bias and noise. You write readable, reproducible Python and SQL, turn open questions into plans and explain your reasoning to people outside your field. Depth in some of these areas matters more than coverage of all of them.
What success looks like:
The team and our customers rely on Reservoir’s core models. Each one has a clear measure of whether it helps the hotel, holds up as data shifts and comes with experiments others can rerun.
Before you apply
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Your profile is current
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Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
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Your notice period
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