Crisp problem framing
User, JTBD, and an explicit non-goal. You can name the prioritization across competing user needs and defend the call.
Product · frame the loop
Prove you can ship the right AI product.
AI product strategy, evals as a product surface, AI roadmapping, metrics design, and human-AI UX. Verified by what you can frame, not what you've read.
Hiring for: AI Product Manager, AI Product Lead, Applied AI PM.
18-question adaptive quiz across problem framing, evals, agentic AI patterns, AI discovery, governance, and launch — plus a graded evals scorecard. ~10h total.
4 skill clusters
The microskills the rubric tests against.
84+ live jobs
AI Product roles in the SignalAI marketplace.
18 months
A verified score is good for 18 months before re-certification.
What you'll prove
The clusters every Product candidate is graded on.
Metrics design
Leading metric (faithfulness, latency, refusal rate) plus a paired guardrail. You name how each metric could be gamed.
Tradeoff analysis with numbers
You picked the chunk size, the model, the latency budget — and you can show the data behind each call.
AI roadmapping
Sequencing experiments under uncertainty. When to invest in evals vs surface vs retrieval. What a hiring PM would actually want to see.
Sample project brief
Author an evals scorecard for a real AI feature
Pick an AI feature shipping in a real product (yours or a public one). Author the scorecard a launch review would actually use.
Deliverables
- · 1-page problem framing with user, JTBD, and non-goal
- · Leading metric, lagging metric, and ≥2 guardrails
- · How each metric could be gamed, and the paired constraint
- · 60-second Loom walking through the scorecard with rationale
How grading works
Transparent rubric. Same bar for everyone.
Each criterion is scored 0–5 with a written rationale. Your score is the weighted sum, published with the rubric so an employer can see exactly what you did.
Problem framing
User and JTBD are crisp. Non-goals are explicit. Prioritization is justified.
Metrics design
Leading + lagging + guardrails. Naming the gameability and pairing the constraint.
Tradeoff analysis
Concrete numbers, not vibes. Picks a fallback for the chosen failure mode.
Narrative quality
Reads like something a hiring PM would forward. Tight, scannable, no fluff.
Live Product jobs
Where AI PMs are hiring right now.
- Apply →Anthropic · working
Product Manager, API Growth
San Francisco, CA | New York City, NY | Seattle, WA
- Apply →Anthropic · working
Product Manager, Monetization
San Francisco, CA | New York City, NY | Seattle, WA
- Apply →Mistral AI · working
Product Manager, Cloud Partnerships
New York, NY
- Apply →Anthropic · working
Product Manager, Claude Code
San Francisco, CA | Seattle, WA
- Apply →Mistral AI · working
Product Manager, Mistral Vibe
Paris
- Apply →Mistral AI · working
Product Manager, Forge
Paris
What a verified profile looks like
Every candidate publishes the same way.
See an example scorecard with the composite score, rubric breakdown, project artifact links, and quiz top-microskills. Yours will look exactly like this.
See a sample profileGet verified
Product candidates take AI Builder Foundations today.
The AIB Foundations assessment grades all three rubrics — including Product — on the same bar. Standalone Product Foundations ships in cohort 02.