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Rox Data Corp
San Francisco, California, United States
Source: Rox Data Corp careers · View original posting
From Rox Data Corp's posting. “We” and “our” refer to the employer.
Why This Role Exists
Foundation models are commoditizing. Defensibility comes from specialized models, proprietary training signals, and evaluation ownership. Every applied AI company we benchmark against like Decagon, Harvey, Sierra, Cursor has already moved. The window to claim frontier applied AI for revenue is closing in the next few months.
Rox is in market. We run agents against enterprise data at scale, every day. We see exactly where research meets production and where the data is dirty, state is changing, and being wrong costs (a lot of) money.
The Applied Research team exists to close that gap permanently.
Cost-efficient inference for Clever Columns.
Distill a Rox-trained model from frontier teachers so per-account enrichment runs at 1/20th the cost without quality loss. Ships first. Doesn't require trajectory attribution.
Signal classification across the public knowledge graph.
A small, fast classifier that distinguishes genuine buying signals from noise across the news, jobs, and filings corpus we already ingest at scale. Powers Recommended Next Moves and Auto Prospecting. Cleanest data subset.
Personalization grounding and hallucination detection.
A reward model that catches fabricated prospect context in Sequences in real time. This is the most underrated production failure mode in outbound AI. Trained on cross-customer consensus edits.
Sequencing policy under sparse, delayed rewards.
Offline-to-online RL on multi-touch trajectories with intermediate signals as proxies for terminal outcomes. Long-horizon flagship. Hard. [Depends on trajectory instrumentation in progress with Platform Eng.]
These are not benchmark problems. They have real SLAs and real customers depending on them.
First few weeks: you understand Rox's architecture, where the production problems are, and where the research gaps are. You have opinions and you share them.
First few months: you are running experiments that directly inform how we build. Something you worked on is in production.
Over time: you are defining the research agenda for the most interesting applied AI problem in the enterprise. The systems you build are things no one else has built before, because no one else has the structural data position to build them.
We are at an unusual moment. Large enough to have real scale, real customers, and genuinely interesting research problems. Small enough that you are one of a handful of people shaping what the Applied Research function looks like and what it prioritizes.
The team is extraordinary: IMO, IOI, and ICPC medalists, researchers from DeepMind and OpenAI. The feedback loop is a live enterprise system, not a leaderboard. If that's not more interesting to you than publishing for the sake of publishing, this probably isn't the right fit.
San Francisco, onsite. We relocate exceptional people.
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