$90-$190/hr AI product management work, on your schedule
Review AI-generated specs, model tradeoffs, and AI-feature strategy the way you'd review a launch built on a model you don't fully control. Flag the spec that ignores hallucination, the metric that won't catch regressions, the UX that breaks when the model is wrong. The judgment for building products on top of imperfect models is exactly what AI labs need.
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Hi, we're Zac and Jack, the founders of Terac. We want to talk to you directly, because you are the most important part of what we're building.
Terac is a community of experts. People who have spent years getting good at something specific and hard. The world is about to need more of you, not less. As AI takes on more of the world's work, the bottleneck shifts to the people who actually know what they're talking about.
Expert labor is the rarest resource in the world right now, and it is shockingly hard to find. The companies that need an AI PM's eye on a feature that ignores how the model fails spend weeks chasing people, paying placement fees, and settling for whoever is available. Meanwhile thousands of qualified people are sitting with knowledge that no one ever asks for.
That gap is what we're here to close. Every project that lands on Terac is routed to the people who actually know the answer, on their schedule, paid fairly, and only when the work is verified. No middleman taking a cut of your time. No vague gigs. No chasing checks.
We care about every single person in this community. If you join Terac, you're not a row in a database to us. We read the feedback. We answer the emails. We will fight for you when a customer is being unreasonable, and we will be honest with you when something on our side is broken. The quality of this panel is our entire company, and we owe you a serious bar.
If you've made it this far, here is what we're asking: claim your profile. Put your expertise on the record. Let the world's most ambitious teams come find you for the work only you can do.
AI Product Management questions
Still curious? Write to us at support@terac.com.
Deep specialization is exactly what makes your responses valuable. We actively seek PMs with focused backgrounds in areas like growth loops, developer platforms, API monetization, or infrastructure products, because frontier models are weakest in precisely those sub-domains. You will be matched to evaluation tasks that fit your specific experience, not asked to fake breadth you do not have.
Formal credentials like PMC, AIPMM CPM, and SAFe POPM are treated as positive signals of structured product thinking, but they are not gatekeeping criteria. What matters most is demonstrated experience writing PRDs, defining success metrics, and making prioritization trade-offs in real products. PMs from non-FAANG backgrounds, including those from startups or regulated industries, are consistently among our highest-rated contributors.
The work is grounded in recognizable product management artifacts. You will evaluate things like AI-written opportunity briefs, user story maps, OKR structures, go-to-market assumptions, and prioritization frameworks such as RICE or MoSCoW scorecards. You may also do conversational interviews where you walk a model through how you would approach a specific product decision, narrating your reasoning the way you would in a staff review.
B2B and enterprise product experience is in high demand because models are systematically undertrained on the nuances of multi-stakeholder buying, procurement constraints, and champion-versus-economic-buyer dynamics. If you have worked with concepts like land-and-expand pricing, compliance-driven feature requirements, or long-tail enterprise customization, that context is genuinely scarce and you will find tasks that draw directly on it.
No task will ask you to reproduce, describe, or evaluate anything tied to your current employer's confidential processes, roadmaps, or internal tools. All evaluation scenarios are constructed from publicly documented product frameworks, open-source tools like Jira and Amplitude, and synthetic product contexts. If a task description ever feels like it is pulling on employer-specific knowledge, you can flag it and it will be replaced.
Why your expertise matters
AI product managers are one of the few professional roles that simultaneously understand what a model can do, what it will do wrong, and how a real product team will try to ship around both constraints. Their judgment on failure mode taxonomy, eval design, and latency/quality tradeoffs is not derivable from engineering or research backgrounds alone, and it is exactly the gap frontier labs need filled when training models to reason about building AI-powered products responsibly. A practitioner who has owned a feature through launch knows where model confidence deceives, where guardrails break UX, and what a regression looks like before the metric catches it - that calibration is the training signal.
How pay works
Pay within the $90-$190/hr band moves with demonstrated hands-on ownership: PMs who have shipped model-powered features to millions of users, written eval rubrics from scratch, or managed a model lifecycle through a major regression command the upper end, as do those with experience at a frontier lab or in a domain where model failures carry regulatory or safety consequences. All work is fully remote and asynchronous, billed hourly, and payment is released only after a Terac reviewer confirms the deliverable meets the task spec - no platform fees, no subscription, no minimum hours required.
What the work looks like
A sample of the AI product management work you would pick up. Every project is scoped, remote, and paid on verified completion.
- Review an AI-generated product spec for a copilot feature and flag every assumption that depends on model behavior the team cannot currently guarantee.
- Evaluate a draft eval rubric for a summarization feature and identify the failure modes it would miss in production traffic.
- Score five competing model outputs for a customer-facing writing assistant against a provided quality rubric, noting where the rubric itself is underspecified.
- Write a worked example showing how you would structure the acceptance criteria for a model-powered recommendation feature, explaining the tradeoff between precision and recall at each latency tier.
- Review an AI-generated incident postmortem for a model regression and assess whether the proposed monitoring changes would have caught the failure earlier.
- Critique an AI-generated launch readiness checklist for an agentic workflow product, identifying missing checks specific to multi-step model execution.
Specialties we match
AI Product Management projects span a wide range of focus areas. Tell us where you go deep and we route the work that fits.
- AI feature scoping and PRD writing
- Eval design and regression benchmarking
- Model selection and capability tradeoff analysis
- Hallucination and failure mode taxonomy
- LLM observability and prompt monitoring (Langfuse, Braintrust, Weights & Biases)
- Human-in-the-loop and fallback UX design
- Responsible AI and policy compliance (EU AI Act, NIST AI RMF)
- Latency-quality-cost optimization
- Fine-tuning vs. RAG vs. prompt engineering tradeoffs
- A/B testing model variants in production
- Agentic workflow product design
- Stakeholder communication for probabilistic systems
