Quant Experts: $100-$300/hr from Frontier AI Labs & Funds

Open application· 36 spots this round

$100-$300/hr quantitative finance and trading work, on your schedule

Review AI-generated quant models, strategies, and derivations the way you'd vet research before it touches capital. Flag the overfit backtest, the look-ahead bias, the derivation that breaks under real assumptions. The skepticism that separates a real edge from a curve-fit is exactly what frontier labs are paying to learn.

Fully remoteYour scheduleWeekly pay

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

Trusted by top research companies

Rated by experts worldwide

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 a quant's eye on an overfit backtest 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

Quantitative Finance questions

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

Deep specialization is exactly what Terac clients want. Tasks are matched to your specific sub-domain, so a rates vol quant reviews prompts and model outputs about swaption pricing and term-structure models, not generic finance questions. The narrower your expertise, the more valuable your correction or critique of an AI output tends to be.

The work is analytical and educational in nature: you are evaluating reasoning quality, annotating worked derivations, or critiquing a model's explanation of a concept such as Greeks calculation or VaR methodology. You are not providing regulated investment advice, managing client assets, or generating live trading signals, so standard securities-regulation scope-of-practice limits do not apply to these tasks.

Tasks span both. Common deliverables include step-by-step derivations (Black-Scholes PDE, stochastic calculus proofs, Monte Carlo convergence arguments), Python or C++ code snippets for pricing engines, and written explanations of risk measures like CVA, DV01, or expected shortfall. You will be told the deliverable type before accepting a task, so you can pass on anything outside your skill set.

Yes. The CFA signals domain credibility, and quantitative portfolio construction is an active area of AI evaluation demand, covering topics like factor-model critique, covariance-matrix estimation, and optimization constraint review. Your Python fluency is a plus because many tasks involve reading or annotating code alongside the financial reasoning.

Opportunities are matched on demonstrated expertise in the task's subject matter, not on credential hierarchy. An FRM with hands-on experience in credit risk modeling, Basel III capital calculations, or stress testing is well-positioned for tasks in those areas. Academic credentials help for highly theoretical derivation tasks, but practitioner depth often outperforms them for tasks grounded in real-world risk frameworks.

Why your expertise matters

Quantitative finance sits at the intersection of stochastic calculus, market microstructure, and statistical inference, and AI models trained without expert review frequently conflate notions of risk-neutral pricing with real-world measure, misapply Ito's lemma, or generate hedging strategies that ignore liquidity constraints and funding costs. Your ability to spot whether a model's reasoning is financially coherent, not just mathematically plausible, is exactly what lab teams cannot replicate from textbooks. Getting derivatives pricing, factor model construction, or VaR interpretation wrong in a training set teaches the next model generation to be confidently incorrect in ways that only a practitioner catches.

How pay works

Rates toward the $300/hr ceiling go to practitioners with demonstrated depth in niche areas: exotic derivatives pricing (barrier options, volatility surface construction, CVA/DVA), systematic strategy development, or regulatory capital modeling under Basel III/IV or FRTB. Work is fully remote and asynchronous - you log in when your schedule allows, complete a defined review or generation task, and payment releases upon Terac verifying the deliverable meets the session criteria. There are no retainers or minimum hours.

What the work looks like

A sample of the quantitative finance and trading work you would pick up. Every project is scoped, remote, and paid on verified completion.

Review an AI-generated Black-Scholes derivation for conceptual errors in the replication argument and flag any conflation of physical and risk-neutral measures.
Evaluate a model-produced delta-hedging strategy for a long gamma position and assess whether the rebalancing frequency and transaction cost assumptions are realistic for the underlying's actual bid-ask spread.
Annotate a set of AI-written explanations of yield curve construction methods, correcting bootstrap sequencing errors and clarifying when interpolation choices materially affect par-swap pricing.
Write a worked example of constructing a Fama-French three-factor regression from daily returns data, including the correct treatment of look-ahead bias in factor portfolio formation.
Score a batch of AI-generated backtests for common overfitting red flags: insufficient out-of-sample periods, survivorship bias in the universe, and Sharpe ratios that ignore autocorrelation in returns.
Draft a reference explanation of initial margin mechanics under SIMM (ISDA SIMMTM), written at the level a senior quant would use to onboard a junior analyst, suitable as training data for a finance-domain model.