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Terac

Founding Engineer

San FranciscoOnsite · United States
FullTime · Engineering
POSTED 17 DEC 2025VERIFIED 10M AGO
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Pay · posted
$140k–210k
mkt median $200k
Open for
322d
since 17 Dec 2025
Hiring · 30d
72
−3 net · open roles
TypeScriptJavaScriptReactNext.jsPythonPostgreSQLAWSLLMs

Founding Engineer

As a founding engineer, you're building the engine that matches every person on earth to the work they're meant to do. This page covers why Terac exists, what you'll own and how we work.

Why Terac exists

Terac stands for The Efficient Resource Allocation Company. Before Uber, you stuck your thumb out and prayed a car would stop. Uber allocated rides efficiently and created opportunity for millions of drivers along the way. Labor is the largest resource in the world, and it's still allocated by luck.

Think about how you got every job you've had. You bumped into the right person at the right time. Most people on earth aren't in the job they'd be best at, and so much talent overseas is completely blocked from work it could do tomorrow.

We believe the best way to change someone's life is to give them work that pays them and fulfills them. We're building the engine that understands people 10 to 100 times deeper than their resume and routes them to the work they're best at.

AI training and market research are where we start, because they're the fastest feedback loop on labor: we know whether a match worked in minutes, not months. The tasks will keep changing. Understanding people and allocating them well is the part that lasts.

Where we are today

Ten months ago we were five people paying experts maybe $20K a year. Experts have now earned more than $3.4M on Terac, working for frontier AI labs and Fortune 500 companies.

We've raised $16M from Emergence Capital, SignalFire, SV Angel and others.

The role

Everyone here works across the whole product. In one week you might improve expert onboarding, debug a payout, tune a recruitment agent and extend a customer integration.

You'll work closely with our Research Customers, Domain Experts, and Strategic Project Leads (SPLs), the people who run customer projects day to day. You find what matters most, build it and take it to production yourself.

Everything you ship touches a real person's work, data or money. When the matching gets better, someone lands better work. When a payout breaks, someone can't pay rent.

What you'll own

The on-demand recruitment engine. Find any niche audience in the world through network traversal, targeted ads, referrals, automated outbound and anything else you can come up with.

Labor profiles and calibration. The exams, paid trials and work signals that show how someone actually works: how fast they start, how careful they are, whether they keep their commitments. Starting within 6 hours of approval already predicts a great expert better than any resume or screener verdict. Make that understanding deeper.

Matching and the marketplace. Opportunity matching, pricing, feasibility estimates, repeat engagement and long-term contract roles. Use what every finished project teaches us to recruit and match better.

Agents. The expert agent that answers experts over text and chat, the AI voice screener, and the agents that assess work, catch fraud and help our team run projects. Measure them with real users and make their actions reliable and reviewable.

The expert experience and their money. Onboarding, project instructions, time tracking, payouts and the Terac card. Terac should be where experts' money lives, not a pipe it passes through.

The Terac API and MCP. How research tools, AI companies and, soon, AI agents call on human expertise. Think of it as the API for human labor.

Everything you ship, in production. Investigate failures across the app, background jobs and outside services, and harden the systems that hold people's work, data and money.

Our stack: TypeScript in a Bun monorepo, Next.js, tRPC, Postgres with Drizzle, Trigger.dev for background jobs, Python on LiveKit for voice agents, and AWS.

Who thrives here

Builders. 3+ years of full-stack work, taking fuzzy problems all the way from investigation to a working product.

Fluent in TypeScript, React and Postgres, and happy to learn any other part of the stack the work calls for.

Experienced with LLMs, voice agents or evals.

Source · TeracFirst seen 2 NovOriginal ↗