Proof I can turn Codex work into team adoption evidence.
I am not pitching generic SWE tenure. I am showing a narrower, inspectable signal: public proof surfaces, verifier code, cleanroom boundaries, and a deployment memo for making AI coding workflows teachable.
Current lane
Primary role: AI Deployment Engineer - Codex, Remote - US. OpenAI's careers page currently lists Codex deployment roles across Technical Success, including the Remote-US lane.
The case is not that I have already worked inside OpenAI. The case is that my work already lives at the customer adoption layer: demos, receipts, failure maps, verifier gates, and handoff artifacts.
Inspect these first
Workshop kit
How I would turn one AI coding run into a scoped team enablement session.
Patterns
Cleanroom technical guide for scope cards, verifiers, receipts, gates, and feedback loops.
30-day plan
What I would produce in the first month: intake, demos, workshop, receipts, and product feedback.
Scorecard
Runnable acceptance gate over role-need coverage, verifier evidence, release gates, and no-outbound boundary.
Assessment drill
Executable interview-signal fixture across design, code quality, performance, tests, communication, deployment judgment, and safety.
Reviewer benchmark
Seven-minute proof route benchmark with source verifiability, route budget, and no-affiliation boundary checks.
Autonomy status
Scheduler health, scorecard gate, public checks, and hard stops for the self-running proof lab.
Field kit
Reusable intake, scope card, demo receipt, decision table, and product feedback templates.
Sample run
A filled public-safe receipt showing the field kit applied to the proof-publication task.
Receipt reference
Runnable source that validates ready/repair/reject runs, feedback packets, repair handoffs, ready second-service handoffs, and simulated product feedback exports.
Reviewer digest
One current route through the proof links, latest receipt, role-fit map, graph path, and send gate.
Route index
The shortest outside-review path: digest, scorecard, ready handoff graph, runnable receipt code, source repo, and boundary.
Memo
How I would turn Codex demos into repeatable adoption evidence.
Role packet
Public mapping from AgentProof to agents, evals, Codex, and developer tooling.
Proof lab
Legally distinct cleanroom repo with Proof Ledger, tests, and CI verifier.
Inspector
Public-safe receipt chain and progress graph with browser-side hash verification.
What each surface proves
- AgentProof AI-built app claims need command checks, browser evidence, screenshots, console failures, and rerun trails.
- Deployment memo Customer teams need a scoped task, demo, failure map, security boundary, and receipt before AI coding scales.
- Workshop kit The deployment story becomes a 90-minute enablement run: scope card, live coding pass, failure map, rollout gate, and handoff receipt.
- Patterns guide The workshop gets a written technical-content layer: scope contracts, context budgets, verifier-first demos, receipts, adoption gates, and product feedback loops.
- 30-day plan The role fit becomes a first-month field trial: intake, demos, workshop, receipt handoff, and product feedback packet.
- Scorecard The first-month field trial gets an executable readiness gate: role coverage, score, release gates, official source URLs, and no-outbound boundary.
- Assessment drill The interview route gets executable proof: a candidate-style deployment response scored across design, code quality, performance, tests, communication, deployment judgment, and operational safety.
- Reviewer benchmark The outside-review route gets a time budget: five public steps, seven comprehension signals, source links, and legal boundary checks.
- Autonomy status The autonomous proof lab checks its own scheduler, heartbeat, public pages, safety scans, and no-outbound gates before future growth work.
- Field kit The first-month plan becomes reusable templates for intake, scope, receipt, ready/repair/reject decisions, and product feedback.
- Sample run The templates are filled on a real public-safe task, including verifier results, misses, decisions, and product signal.
- Receipt reference The receipt pattern becomes code: ready/repair/reject fixtures, validator, markdown renderer, feedback packet, repair-state handoff, ready-state second-service handoff, simulated feedback export, unit tests, and CI.
- Cleanroom repo The proof-lab concept is public, non-affiliated, testable, and explicit about not copying OpenAI identity or systems.
- Proof Ledger A public-safe inspector verifies the projected hash chain and shows the current mission graph.
- GitHub Actions The public proof-lab repo verifies itself in CI instead of relying on narrative-only claims.
Why this maps to Codex deployment
- Codex adoption is a workflow design problem, not only a model capability story.
- Reference implementations should include failure traces and review gates, not just polished demos.
- Customer feedback needs to separate model behavior, repo context, tool harnesses, permissions, acceptance criteria, and simulated signal from real deployment claims.
- A deployment engineer needs proof artifacts a team can rerun after the workshop ends.
Cleanroom line
This page is independent from OpenAI. It uses OpenAI and Codex only to name public target roles and source context. It does not use OpenAI logos, product UI, private systems, or affiliation language.
Sources checked: OpenAI Careers search, AI Deployment Engineer - Codex, and OpenAI brand guidance.