Rox Data Corp

Rox Data Corp

Founding Applied Research Engineer

San Francisco

Sponsorship not specifiedDetected 54 days ago
Machine LearningRAGMLOpsOutbound SalesResearch

About the role

  • 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.

Responsibilities

  • Design and run research programs tied directly to the four above.
  • Build evaluation frameworks that measure trajectory quality, not just final output, because most eval infrastructure measures end results and we care about the path.
  • The systems you build are things no one else has built before, because no one else has the structural data position to build them.

Requirements

  • You have spent real time thinking about how agents fail in practice, not just on benchmarks.
  • You have built evaluation systems and know exactly where standard approaches break down.
  • A PhD is not required.
  • Strong research instincts and the ability to ship are.
  • You have opinions and you share them.
  • Over time: you are defining the research agenda for the most interesting applied AI problem in the enterprise.
  • Small enough that you are one of a handful of people shaping what the Applied Research function looks like and what it prioritizes.

Company info

  • Foundation models are commoditizing.
  • 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.
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