Backend Engineers: $70-$200/hr from Frontier AI Labs

Open application· 118 spots this round

$70-$200/hr backend engineering work, on your schedule

Review AI-generated services, APIs, and system designs the way you'd review a teammate's PR. Catch the race condition, the N+1, the design that won't survive scale. The judgment that ships systems which stay up under real traffic is exactly 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 a backend engineer's eye on a race condition under load 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

Backend Engineering questions

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

Specialists in distributed systems, message brokers like Kafka or RabbitMQ, and consensus protocols are among the most requested profiles because frontier models struggle most with nuanced tradeoffs in that domain. You will likely be asked to evaluate model-generated designs for things like partition tolerance decisions, backpressure handling, and idempotency guarantees - exactly the territory where generalist answers fall short. Narrow depth is an asset, not a limitation.

You will not review proprietary client systems or live infrastructure. The code, architecture diagrams, and configuration snippets you evaluate are either synthetically generated or sanitized scenarios created specifically for model training, so there is no risk of inadvertently advising on a real production environment. Your role is to assess the quality of AI-generated output, not to audit or certify any actual system.

Certifications such as CKA, CKAD, AWS Solutions Architect, or Google Cloud Professional Cloud Architect are useful signals but are not required for most tasks. What qualifies you is demonstrated ability to reason about the relevant domain: container orchestration, cloud networking, database internals, API design, and so on. If a particular task requires deep Kubernetes expertise, for example, holders of CKA or CKAD will be prioritized, but equivalent professional experience reviewing or building those systems carries the same weight.

The specific deliverable type varies by task, but common formats include REST and gRPC API designs, SQL and NoSQL schema definitions, system design write-ups covering scalability and fault tolerance, and code in languages like Go, Python, Java, or Rust. Some tasks ask you to rank two competing implementations of the same service and explain the tradeoff; others ask you to write a worked solution yourself that demonstrates expert-level reasoning. You will see the exact format before committing to a task.

Data engineering sits squarely within the backend domain on Terac, and pipeline-focused work is actively needed because models are frequently trained and evaluated on data infrastructure tasks. If your expertise centers on batch and streaming pipeline design, orchestration with Airflow or Prefect, or transformation layers built with dbt or Spark, that qualifies you for a subset of tasks that pure application-backend engineers are less equipped to evaluate. You can indicate your sub-specialties during onboarding so the platform routes the right tasks to you.

Why your expertise matters

Backend engineers build the systems that AI models increasingly generate code for, from REST APIs and gRPC services to database schemas and distributed job queues. When a model suggests a database index strategy, proposes a connection pooling configuration, or generates an async task worker, only a working backend engineer can reliably judge whether that output is correct, idiomatic, and safe under production load. Without expert review, models learn from plausible-looking code that silently violates consistency guarantees, introduces N+1 query patterns, or misuses transaction isolation levels in ways that only surface at scale.

How pay works

Pay within the $70-$200/hr band scales with the depth of your specialization: engineers with strong opinions on distributed systems design, database internals, or high-throughput messaging systems (Kafka, RabbitMQ, SQS) tend to land toward the upper end, as do those who can articulate precise failure modes rather than just flag that something "looks wrong." Work is fully remote, billed hourly, and released on verified task completion - there are no fixed shifts, and you can pick up tasks around your existing job.

What the work looks like

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

Review an AI-generated PostgreSQL migration that adds a foreign key to a high-write table and flag whether the lock behavior would cause downtime in production.
Evaluate a model-written async worker that processes Stripe webhooks and identify whether idempotency and retry logic are correctly implemented.
Score three AI-generated REST API designs for a pagination endpoint and rank them on correctness of cursor-based vs. offset approaches for large result sets.
Write a worked example showing how you would diagnose and resolve a connection pool exhaustion issue in a Node.js service backed by PostgreSQL.
Review an AI-generated Kubernetes deployment manifest for a stateless API service and identify misconfigured liveness probes, resource limits, or missing graceful-shutdown handling.
Assess a model-produced Redis caching layer for a user session service and determine whether the cache invalidation strategy is consistent with the described write patterns.