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Hire Bret Kerr · AI GTM Engineer

AI GTM Engineer

GTM tooling built with Claude Code: skills, agents and pipelines where agents propose and deterministic gates dispose.

  1. Check 01

    Ships production code with Claude Code, not just prompts: skills, subagents, verification gates.

  2. Check 02

    Has built GTM and job-search tooling end to end: ApplyForge, Dashboard Builder, MMPipe.

  3. Check 03

    Instruments pipelines so numbers are recomputed, not asserted: Transformer Atlas, Skill Memory Bank.

The checklist · typical requirements, each mapped to a live proof

  1. 01

    Builds with Claude Code in production

    Claude Code skills →

    Seven Claude Code skills for the MoEA deep-research pipeline: paste messy research, get an interactive dashboard plus three publish-ready assets in about 60 seconds. Apache 2.0.

  2. 02

    Delivers under time pressure

    Skill Memory Bank →

    Open Accelerator Hackathon, Lane 2 Best in Show. Agent Build Day 2026, June 27, Fort Point, Boston. Red Hat · IBM · Mass AI Hub.

  3. 03

    Ships GTM and workflow tooling

    ApplyForge →

    A local-first iOS job-search co-pilot: track every application in a Cockpit, batch-tailor résumés and cover letters in the Forge. Free, bring your own key. See also the Dashboard Builder at /labs/job-prep-dashboard-builder.

  4. 04

    Designs multi-model research loops

    MoEA loop →

    A typed rewrite system that drives three skills into an auditable research tree, a labeling harness (/MoEA-labeler), and a captured Sonnet reasoning trace (/SonnetSkillReasoning).

  5. 05

    Verifies instead of trusting

    Transformer Atlas →

    GPT-2 small through TransformerLens: form a circuit hypothesis, ablate a head, rerun the forward pass, measure. Every number is recomputed from the real residual stream.

  6. 06

    Turns long-form input into structured output

    YouTube Explainers →

    About ten hours of interview tape compressed into four source-anchored explainers, six pairwise analyses, and one reusable URL-to-explainer skill.

Three deep proofs

Skill Memory Bank page

Hackathon · Best in Show

Skill Memory Bank

A governed memory control plane that compiles conversations into SKILL.md packages.

Proves · Can scope, build and demo an agent system in a day, with a judging panel as the gate.

Lane 2 Best in Show · Red Hat / IBM Open Accelerator Hackathon, June 27 2026

Open it live →
YouTube Explainers page

Pipeline · video in, instrument out

YouTube Explainers

Gemini ingests the YouTube URL natively; Claude builds a source-anchored explainer; a correlation matrix lets the interviews argue with each other.

Proves · Multi-model orchestration shipped as a reusable skill, not a one-off demo.

~10 hr of tape · 4 explainers · 6 pairwise analyses · 1 skill

Open it live →
Transformer Atlas dashboard, looping

Mechanistic interpretability · runnable

Transformer Atlas

GPT-2 small in the browser: form a hypothesis, ablate an attention head, and measure whether the prediction moves.

Proves · Comfort with model internals and with instruments whose numbers are recomputed, not quoted.

Every number recomputed from the real residual stream

Open it live →

How I work · Signal → Thesis → Test → Ship

  1. 01 · Signal

    Opposed deep-research briefs (Gemini as cartographer, GPT as adversary), then cite-checked and reconciled.

  2. 02 · Thesis

    One claim per piece, anchored to primary sources and written in the house voice. Story Extraction and Content Architecture, from the manifesto.

  3. 03 · Test

    Deterministic gates, not model consensus, decide what ships. Quotes are verified, numbers are recomputed, and goldens are locked.

  4. 04 · Ship

    One operator. Cloudflare Workers, a parity-gated deploy, and a public commit history.

The full methodology: Agentic Content Marketing →

Contact

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