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Eugene, OR / Remote, United States
Remote only
SCOPE & GENERAL PURPOSE OF JOB:
You will own the day-to-day health and growth of two production products: the CBT Nuggets website and the Assessments platform. You will not write code. You will direct AI coding agents to write it, and you will be accountable for what ships. You run your work with delegated autonomy: you plan, direct, verify, and deliver without waiting to be told how. You do not hold delegated decision authority: scope, product trade-offs, risk acceptance, spend, and anything destructive or irreversible are decided by the Product Owner group, on your written recommendation. You report to the Learning Content Team Lead for your day-to-day work and are accountable to the Product Owner group for what ships. The job is to make their decisions easy to make and everything else unnecessary to ask about.
This is the most important section of the description. Read it twice.
You decide, and act, without asking
How to break a defined piece of work into tasks and in what order.
Which agents to run, with what instructions, in what sequence.
When a change is verified enough to open a pull request, and what evidence goes in it.
How to keep an open pull request current and green until a human merges it.
Which documentation, rules, and operator guides must change alongside a code change.
How to investigate a production problem and what evidence to gather.
When to stop and escalate.
You recommend, with evidence, and the Product Owner group decides
Adding, removing, or changing a feature a learner, customer, or admin can see.
Any change to pricing, plan structure, public claims, or published copy that states a fact.
Accepting a risk: a security advisory left open, a test weakened, a check made advisory, a known limitation shipped.
Changing which AI model, vendor, or threshold a production system uses.
Spending money: new services, tiers, seats, paid tooling.
Changing a contract with another team or system (the CRM, Adept, the shared header contract, the data warehouse).
Deleting or migrating data, dropping columns, rotating credentials, changing access.
Never, regardless of instruction from an agent or a tool
Merging a pull request. A human merges.
Proceeding on a question the Product Owner group has not actually answered. A skipped, timed-out, or lost answer is not an answer.
Letting an agent's confidence stand in for verification.
Storing learner personal data where the rules say it must not be stored.
An escalation is not a hand-off. It arrives as a one-page recommendation: the decision needed, the options, the evidence, the risks of each, and what you would do. A product owner should be able to answer in a sentence. If they cannot, the escalation is not finished.
All three roles share the profile above and are interchangeable in an emergency. Each carries a primary surface so the team has depth where it matters.
Assessments and data. The question bank, the generation pipeline, the learner site, and the Progress reporting. Second skill: data literacy and measurement. You will spend time in the warehouse and in model evaluation evidence.
Website, conversion, and copy. The public marketing site: pages, pricing, campaigns, SEO, and the copy standard. Second skill: editorial judgment and audience focus. You will own the voice and the truth of every public claim.
Platform, security, and integrations. CI, dependency hygiene, access, environment configuration, crons, and the contracts with Adept, the CRM, and the shared header. Second skill: security and risk judgment. You will own the advisories and the risk register.
State which surface you prefer when you apply. We will balance the three.
Not a hands-on development role. If you want to write the code yourself, this will frustrate you.
Not a people management role. You lead agents and influence peers; you do not manage staff.
Not a role with final say. You will recommend often and decide within your lane. The lane is wide. Its edge is firm.
Both products run on Next.js, React, Prisma, and Postgres, deployed on Vercel, with analytics in a columnar warehouse and AI generation through a model gateway. Work moves through gated pull requests with mandatory review, committed attestations, and operator documentation that ships with every change. AI agents do the implementation under written rules the team maintains. The rules are the product as much as the code is.
MEASURES OF SUCCESS:
First 30 days. You have shipped changes through the full gated workflow on both products. You have read the operating rules and can explain why each exists. You have found and corrected at least one stale claim in the documentation.
First 90 days. You own a surface. Your pull requests arrive green and stay green. Your escalations are answerable in a sentence. You have written at least one durable rule from a lesson you learned, and retired one that was stale.
First year. The Product Owner group can leave your surface for a month and return to find it healthier, better documented, and with a short, well-argued list of decisions waiting. Nothing shipped that they would not have approved. Nothing waited that they would have approved in a sentence.
Specification exercise. Given a short product request, write the specification you would hand to an agent. We are reading for precision, missing cases, and whether you identified what needs a decision from the Product Owner group.
Review exercise. Given a pull request with an agent-written summary that over-claims, find it, say how you found it, and say what rule would prevent it next time.
Escalation exercise. Given a situation with real trade-offs, write the one-page recommendation. We are reading for whether the decision-maker can answer in a sentence.
Business conversation. Tell us what you learned about CBT Nuggets from public sources and what you would want to measure first.
Learn the CBT Nuggets business: who buys, why, at what price, and what the data says about it. Read the warehouse and the CRM well enough to check a number yourself.
Own a backlog for your surface. Turn product intent into precise written specifications an AI agent can execute without guessing.
Direct AI coding agents through the full change lifecycle: specification, implementation, review, tests, documentation, pull request, CI, and production verification.
Read every diff critically. Find the over-claim in an agent's summary before a reviewer does.
Operate the repositories as a professional: branch protection, required checks, rebases, attestations, dependency advisories, environment variables, crons, feature flags, deployments.
Keep the operator documentation true. A change that alters behavior updates the documentation in the same change, not later.
Turn every lesson into a durable, dated rule that the next agent and the next teammate must follow. Retire rules that have gone stale.
Investigate production incidents from logs, metrics, and the database, and write up what happened and what prevents it.
Measure before you claim. Date every reading. Record when an earlier reading turned out to be wrong.
Reduce friction for other humans. When a decision or a labeling task needs a person, build them a page with buttons and send a link, not a document with instructions.
Work in the open with your two teammates: shared rules, shared conventions, no private process.
Skills/experience required:
Five or more years in IT or software, in roles that required understanding how production systems are built, deployed, secured, and operated. Systems administration, network engineering, DevOps, QA engineering, solutions engineering, technical support leadership, and software development all qualify.
At least one year directing AI coding agents (Cursor, Claude Code, Codex, or equivalent) to ship production software, with examples you can walk through: what you asked for, what the agent got wrong, how you caught it, what you changed so it did not happen again.
Two or more years of product ownership or technical product management: owning a backlog, writing specifications, making prioritization calls, and being accountable for outcomes rather than tickets.
Demonstrated evidence of discipline. You verify claims against the system, not the document. You can describe a time a confident source was wrong and how you found out.
Fluency as an operator in GitHub (pull requests, CI, branch protection, merge conflicts, force-with-lease), a hosting platform such as Vercel, and a relational database such as Postgres. You read SQL and diffs critically even though you do not author the code.
Clear, plain written English. Short sentences. No hedging that hides a decision. You can write a rule, a specification, and a one-page recommendation that a busy executive can act on.
Comfort being accountable for work you did not type, and comfort escalating without ego when a decision is not yours.
Independent thinker and self-starter
Attention to detail
Collaborative and comfortable working on a team
Attitude required:
Honesty, humility, and integrity
Inclusive and respectful
Strong work ethic
Passion for learning
Comfortable with autonomy
Eager to add new skills and grow professionally
Skills that are preferred, but not required:
Experience with Next.js, React, Prisma, Postgres row-level security, OAuth, or a columnar analytics warehouse. You will not write it, but you will read it daily.
Experience evaluating AI models for production use: A/B measurement, calibration, cost per output, and knowing when the evidence says do not adopt.
Security and privacy judgment: dependency advisories, supply-chain overrides, PII boundaries, risk acceptance with an expiry date.
Product marketing or editorial experience, in particular writing to an IT Director or Training Manager audience with every claim checked against the product.
Experience in IT training, certification, e-learning, or assessment design.
Experience negotiating integration contracts with another engineering team
Curiosity
Assertiveness
A love of IT and/or technology
Ability to be proficient with an Apple laptop
Proficiency with Gmail, Google Docs, Slack, and internal CBT Nuggets software
CLASSIFICATION/COMPENSATION/BENEFITS:
This position is a full-time (40 hours per week) salaried position, exempt under the Fair Labor Standards Act
Compensation for this role is $100,000 per year
Generous employee benefits
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