The public AI PM portfolios and portfolio archetypes that show what hiring managers actually want to see in 2026 — case studies, evals, trade-offs, not buzzwords.
What Hiring Managers Actually Look For
The bar for an AI PM portfolio has moved sharply in 2026. A list of AI features you shipped is no longer enough. Hiring managers at OpenAI, Anthropic, Notion, Linear, Stripe — anyone hiring serious AI PMs — want to see four things: the user problem framed with specifics, the model decisions made and why, the evals used to measure quality, and the trade-offs accepted. Without those four, your portfolio reads as someone who watched AI happen rather than someone who shipped it.
Below are the named portfolios worth studying plus the portfolio archetypes that hiring managers consistently respond to. Where a specific portfolio is publicly findable, the URL is included. Where the value is in the format rather than the individual, the archetype is described so you can build your own version.
📂Portfolio building is half the job hunt. The AI PM Masterclass includes portfolio reviews with a Sr. Director PM and 2 real AI projects you can put on your portfolio.
Named Public Portfolios
1. Marily Nika (AI PM at Google, Author)
Marily Nika's personal site doubles as a portfolio of her AI PM work at Google, Meta, and her book "Building AI-Powered Products." Each case study includes the customer problem, the model decisions, and outcomes with concrete numbers. The format is straightforward — narrative prose with embedded screenshots — but the substance is exceptional.
What to learn: the level of specificity in her case studies. She names the model, the eval metric, the baseline, and the lift. That is the standard you should hold your own portfolio to. Most candidates fail this bar dramatically.
Why AI PMs need this: The clearest public example of how to write an AI PM case study with technical specificity.
View Portfolio2. Aman Khan (AI PM at Arize, Writer)
Aman Khan publishes deeply technical AI PM content on Substack and on Arize's blog, which functions as a public portfolio. His writing on LLM evals, observability, and AI product metrics is referenced by hiring managers as "this is what an AI PM should be able to write."
What to learn: writing as portfolio. If you can publish 5–10 substantive posts on AI PM topics, you have built a portfolio that travels further than any Notion page. The bar is genuine analysis with real data, not opinion pieces.
Why AI PMs need this: Proof that technical writing can be the strongest possible AI PM portfolio.
View Portfolio3. Aakash Gupta (Product Growth Guy, Substack + Maven)
Aakash Gupta's Substack and Maven course pages function as a portfolio of his AI PM thinking. His PRD templates, deep dives on AI PM interview questions, and case study breakdowns are widely shared by hiring managers internally as reference material.
What to learn: artifacts as portfolio. Real templates, real frameworks, real interview decks, all publicly available. Hiring managers see them and assess "this person thinks about AI PM craft seriously." That is the only signal that matters.
Why AI PMs need this: Best public example of using artifacts (templates, frameworks, decks) as a portfolio rather than case studies.
View Portfolio4. Linus Lee (Notion AI, Personal Site)
Linus Lee's thesephist.com is part personal site, part research portfolio, part demo gallery. He works on Notion AI and his public artifacts include live demos of AI interaction prototypes, written explorations of generative interfaces, and code experiments. Closer to a researcher portfolio than a classical PM portfolio.
What to learn: prototypes as portfolio. The most senior AI PM roles in 2026 increasingly expect candidates to ship small demos, not just documents. A working Vercel-hosted demo with three slides of context outperforms a polished 20-page case study at the labs.
Why AI PMs need this: The gold standard for demo-based AI PM portfolios. Aspirational and hard to match, but the right direction for ambitious AI PMs.
View PortfolioHigh-Signal Portfolio Archetypes
5. The "Three Deep Case Studies" Notion Portfolio
The default format for AI PM portfolios in 2026: a Notion page with three deep case studies, each 1,500–2,500 words, each following the structure problem / approach / decisions / evals / outcomes / what I would do differently. Linked from your LinkedIn, sent in initial application emails.
What makes the strong version different from the weak version is specificity. Strong: "We chose Claude 3.5 Sonnet over GPT-4o because in our eval set of 200 customer support tickets, Sonnet scored 0.87 on JSON formatting compliance versus 0.79." Weak: "We chose the best LLM for our use case." Hiring managers can spot the difference in 30 seconds.
Why AI PMs need this: The minimum-viable portfolio format. Build this first. See our deep dive on the AI PM portfolio guide.
6. The "Built-It-Myself" GitHub + Live Demo Portfolio
A GitHub profile with 2–3 working AI projects (deployed on Vercel, Replit, or Modal) plus a README per project explaining the product reasoning behind it. Increasingly common among AI PMs from technical backgrounds and increasingly expected by AI labs.
The differentiator is the PM thinking documented in each repo. A README that explains "I tried three retrieval strategies; here is the eval I used to compare them; here is why I picked the one I did" is rare and valuable. Code can be vibe-coded — the thinking cannot.
Why AI PMs need this: The fastest-rising portfolio format among AI PM hires at the labs. Distinguishes you from PMs who only talk about AI.
7. The "PRD + Eval Pack" Portfolio
A single Notion or PDF that contains one or two complete PRDs for AI features — the kind you would write at work — plus the eval set you would use to measure them, plus a one-pager on how you would roll out the feature. No marketing fluff.
Hiring managers love this format because it tests exactly the work you would do on the job. The risk is that you need to be confident your artifacts are above bar — a weak PRD here is worse than no PRD at all. Get feedback from a senior AI PM before publishing.
Why AI PMs need this: Closest format to actual on-the-job work. Best for candidates with strong AI PM writing instincts.
8. The "Eval Library" Portfolio
A public repo or document containing 10–20 evals you have designed for AI products — input examples, expected outputs, scoring rubrics. Some are for hypothetical products, some are for products you have actually worked on (with appropriate redactions).
Highly specialized but extraordinarily powerful for evals-focused roles (at the labs, at AI infrastructure companies, at any team building agents). The number of AI PMs who can demonstrate eval design fluency in 2026 is small, and hiring managers actively seek them out.
Why AI PMs need this: Niche but high-leverage. Best portfolio format for AI infrastructure and agent-team PM roles.
9. The "Public Writing as Portfolio" Substack
Following the Aman Khan / Aakash Gupta model: a Substack with 8–15 posts on AI PM topics. The posts are the portfolio. No separate case studies, no PDFs to download. Linked directly from your LinkedIn headline.
Works because hiring managers can read 2–3 posts and quickly assess whether you understand AI product management. The catch is that you need to actually have something to say — recycled "5 lessons from shipping AI" posts will hurt you, not help.
Why AI PMs need this: Highest-leverage portfolio format if you can write substantively. Compounds over time as you publish more.
10. The "Internal Project Anonymized" Portfolio
When your work cannot be shared publicly (most AI PM work falls here), the right format is an anonymized case study: customer / company is generic ("a B2B SaaS customer support tool"), feature is described in mechanics not branding, numbers are real but framed as percentages and lifts rather than absolute counts.
Hiring managers expect this format from PMs at companies with confidentiality requirements. The risk is over-anonymizing into vagueness. Keep the specificity of the model decisions, eval design, and trade-offs — only anonymize the brand and absolute revenue numbers.
Why AI PMs need this: Required format for most working AI PMs whose actual projects are confidential.
Starting Points and Templates
11. Notion Product Manager Portfolio Templates
Notion's template gallery has several PM portfolio templates that are reasonable starting structures, though none are AI-specific. Pick a clean one and adapt the case study structure to AI specifics — add sections for model selection, eval design, and rollout strategy.
The template is the easy part. The hard part is the content quality of the case studies inside. Do not spend more than two hours on visual polish before you have written the actual case studies.
Why AI PMs need this: Practical starting point for the "three deep case studies" format above.
View TemplatesThe 30-Second Test
A hiring manager will spend 30 seconds on your portfolio before deciding whether to read further. The 30-second test: do they see a specific AI product, a specific eval metric, and a specific outcome number? If not, your portfolio fails at the screening step and never gets to the deep read. Optimize the top of every case study for those three things.
Common Portfolio Mistakes
The most common failure mode is treating the portfolio as a marketing brochure. Glossy hero images, "I am passionate about AI" copy, vague impact statements. Hiring managers tune out within seconds. The portfolio is a technical credibility instrument, not a personal brand statement.
The second most common failure is over-using AI screenshots. A screenshot of ChatGPT or a Claude response is not a portfolio artifact. The artifact is your reasoning about why that product behavior was the right one to ship, what alternatives you rejected, and how you measured success.
The third failure is missing the trade-offs section. Every real AI shipping decision involves trade-offs (cost vs. quality, latency vs. accuracy, hallucination risk vs. coverage). Portfolios that document these trade-offs honestly are dramatically rarer than they should be, and dramatically more valued.
How to Build Yours
Start with one case study. Pick the AI feature you know best — at your current job, in a side project, or from a course capstone. Write it in the problem / approach / decisions / evals / outcomes / what I would do differently format. Aim for 1,500 words.
Get feedback from one senior AI PM before publishing. The feedback will hurt. Apply it. Then write your second case study using what you learned from the first round of feedback. The second case study will be twice as good as the first.
Three deep case studies, plus your LinkedIn pointed at them, is enough. You do not need a personal website, a custom domain, or a designed PDF. Substance over polish, every time.
Where Portfolios Fit in the Job Hunt
Your portfolio gets you past the screening step. It does not get you the offer — interviews do. But without a strong portfolio, you rarely reach the interviews where you could close. The portfolio is a necessary precondition, not a sufficient one.
Pair it with the right job boards (see our best AI PM job boards guide) and rigorous interview prep. The job hunt is a system; the portfolio is one component.
Where to Get Hands-On Feedback
The fastest portfolio improvement comes from feedback by someone who has hired AI PMs. Our AI PM Masterclass includes structured portfolio reviews with a Salesforce Sr. Director PM and 2 real AI projects you can put on your portfolio — both delivered in 4 weekends rather than 4 months of self-study.
That feedback loop is the difference between a portfolio that gets you screened in versus one that gets you screened out. The compounding effect over a six-month job hunt is enormous.
Start Tonight
Open a Notion page. Title it "Portfolio - Draft." Write the heading of your first case study and the problem statement. That is the entire commitment for tonight.
Within two weeks of consistent writing, you have a draft case study. Within a month, you have feedback applied and a second case study underway. Within a quarter, you have a real AI PM portfolio. The AI PMs who land great roles in 2026 are the ones who started writing tonight, not the ones who planned perfectly and started next quarter.