FP&A Analysts: $65-$150/hr from Frontier AI Labs

Open application· 58 spots this round

$65-$150/hr FP&A and financial planning work, on your schedule

Review AI-generated forecasts, operating models, and variance analyses the way you'd review a draft before it goes to the CFO. Flag the assumption that breaks the model, the variance no one can explain, the driver that's quietly wrong. The judgment behind a number leadership actually trusts is what AI labs need.

Fully remoteYour scheduleWeekly pay

Apply nowApply once, get matched on a rolling basis. No prior AI experience needed.

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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 FP&A analyst's eye on a forecast that doesn't tie out 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.

Zac & Jack

Founders

FP&A questions

Still curious? Write to us at support@terac.com.

SaaS and recurring-revenue modeling is one of the more in-demand sub-specialties because AI models frequently produce forecasts built around ARR, NRR, and cohort-based churn that need review from someone who works with those constructs day-to-day. Generalist corporate FP&A backgrounds are equally welcome. What matters is that you can evaluate whether a model's reasoning about a specific domain is sound, not that you cover every domain.

The FPAC is a recognized credential for this work, and you do not need a CPA license or CFA charterholder status. Terac values demonstrated FP&A expertise, so the FPAC, the FMVA from CFI, or equivalent verifiable experience in financial modeling and planning all count. Credential type is used to route you to relevant tasks, not to gate you out of the program.

Your role is to evaluate whether the AI's reasoning and outputs are accurate and well-structured, not to sign off on anything with legal or regulatory effect. You will not be issuing opinions that bind a company, and the work product is used for model training, not for filing or compliance purposes. If a task touches a sensitive area like ASC 606 or SOX narratives, you flag errors in the model's reasoning the same way you would in a peer review.

Tasks are drawn from the core FP&A deliverable set: budget-versus-actual variance commentary, driver-based forecast models, scenario and sensitivity analyses, and the financial narrative sections of board or investor packages. You may also review worked examples where the AI explains its own forecasting logic, which requires you to assess whether the reasoning from assumptions to outputs is coherent and reflects how practitioners actually build these models.

Platform familiarity matters less than your ability to evaluate financial logic, model structure, and forecast methodology. Most AI-generated work you review will be presented as formulas, structured outputs, or written analysis rather than live files in any specific tool. If tasks do involve tool-specific content, Anaplan and Adaptive Planning experience is an asset because those environments have their own modeling conventions that differ from Excel and that generalist reviewers may not catch.

Why your expertise matters

FP&A work requires the kind of multi-step numeric judgment that AI models notoriously get wrong in subtle ways: a model can produce a plausible-looking variance bridge or three-statement model that violates basic accounting identities, misapplies period conventions, or confuses run-rate with reported figures. Catching those errors requires someone who has closed a forecast cycle, argued a budget to a CFO, and learned what "wrong" looks like before a board presentation. Your ability to distinguish a coherent operating model from a superficially formatted one is exactly the signal labs need to close that gap.

How pay works

Rates toward the top of the $65-$150/hr band go to practitioners with hands-on experience building and auditing multi-year financial models, consolidating across business units, or working in regulated environments where GAAP or IFRS treatment matters. All work is fully remote, billed hourly, and released to you only after your submission is verified complete - there are no unpaid "waiting periods" between task acceptance and payment.

What the work looks like

A sample of the FP&A and financial planning work you would pick up. Every project is scoped, remote, and paid on verified completion.

Review an AI-generated three-statement model for a SaaS company and flag any cells where the cash flow statement fails to reconcile with net income and balance sheet movements.
Evaluate an AI-produced budget narrative and identify where stated variances contradict the supporting schedule or use the wrong sign convention for favorable versus unfavorable.
Write a worked example of a driver-based headcount plan for a 200-person company, showing how to flow hiring timing into compensation expense, payroll taxes, and benefits accruals month by month.
Assess whether an AI explanation of contribution margin versus gross margin is accurate for a manufacturing business that capitalizes some overhead, and correct any conflation.
Create a labeled scenario analysis comparing base, upside, and downside revenue cases, annotating each assumption so a model can learn the reasoning behind the range rather than just the numbers.
Score a set of AI-generated cash conversion cycle explanations for a retail client on whether they correctly distinguish days sales outstanding, days inventory outstanding, and days payable outstanding in context.