Careers

Build the performance layer for the agentic shopping era.

We are a small team building Lantern in New York. The work sits at the intersection of model evaluation, commerce systems, and design for serious operators.

What we work on

Three threads of work, one operating loop.

Each thread compounds. The systems team makes the answers observable; the commerce team turns signals into shippable work; the measurement team proves what moved.

Thread 01

Make AI answers observable

Build the citation telemetry that runs buyer-intent prompts across ChatGPT, Claude, Perplexity, and Gemini, and explains why a recommendation moved.

Thread 02

Translate signals into commerce work

Turn Citation Score, External Signals and Industry Changes into recommendations a DTC team can ship this week.

Thread 03

Prove what moved

Connect every Applied recommendation back to the Brand AI Health delta it produced. The audit log IS the product.

Operating principles
  1. 01

    Write down the argument before building the interface.

  2. 02

    Prefer a small loop with real customer evidence over a large abstract roadmap.

  3. 03

    Treat design, data quality and reliability as product work, not polish.

  4. 04

    Keep the output useful to the operator who has to make a decision this week.

Open roles
EngineeringFull-stack engineer · model & evalsRemote, NYC-overlapEngineeringFrontend engineer · citation telemetryRemote, NYC-overlapProductProduct designer · foundingNYC or remoteGo-to-marketFounding GTM · DTC operator backgroundNYC
Run the loop

See where AI recommends competitors. See what to fix first.

No store access. No signup. The first scan lands as a board-ready brief, not another dashboard.

What to include
  • The role or area you are interested in
  • Examples of hard work you made clearer
  • Where you are based and when you could start

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