Software Engineer, Frontier Data Products

Mercor

16d•$130,000 - $500,000•Onsite

About The Position

Mercor is defining the future of work. We partner with leading AI labs and enterprises to provide the human intelligence essential to AI development. Our vast talent network trains frontier AI models in the same way teachers teach students: by sharing knowledge, experience, and context that can't be captured in code alone. Today, more than 30,000 experts in our network collectively earn over $2 million a day. Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our new San Francisco headquarters. Frontier AI companies are increasingly bottlenecked on expert judgment and high-quality data workflows. This team builds the production systems that capture, coordinate, and validate that work at scale — directly between a customer request and the output that ships. These are long-running, stateful systems. A single job can stay live for days, interleaving automated steps, model inference, and expert review. A step marked "done" can be reopened, re-reviewed, and redone — so "completed" is not always final, state has to tolerate late mutation, and correctness has to survive humans and models disagreeing with each other. This is a backend systems and orchestration problem: distributed state machines, not pipelines. The architecture is not set. Early engineers will decide what it becomes, and the loop between "I shipped this" and "this mattered" is short.

Requirements

Production backend experience with strong opinions about what ages well and why
Sharp instincts for system design, service boundaries, and where to put complexity — and where to refuse it
Fluency with the distributed systems toolkit: async workflows, queues, idempotency, retries, and long-running jobs as practice, not resume line items
Ability to take ambiguous product, operational, and ML constraints and turn them into a system that is clean and debuggable
Comfort working in Python on AWS with Postgres

Nice To Haves

experience with Temporal or similar workflow engines is a plus

Responsibilities

Design services and state models for multi-stage workflows that fan out across automated processing and expert reviewers, then reconcile results into a coherent whole
Build orchestration primitives — retries, failure recovery, idempotency, auditable state transitions — for jobs that run far longer than a request and can be partially redone after the fact
Integrate model inference into production workflows without sacrificing debuggability or human oversight
Build the APIs and tooling that let product, operations, and ML teams operate, debug, and trust these systems at scale
Own reliability and observability for workflows where a silent failure means a corrupted result, not just a 500
Moving fast on genuinely hard systems problems — ambiguity is the default, not the exception
Working closely with product, operations, and ML teams to translate a tangle of constraints into clean system design
Debugging complex stateful workflows where the failure surface spans automated steps, model calls, and human reviewers
Owning your systems end-to-end: design, ship, operate, improve