Job description

ABOUT MERCOR Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents. 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 San Francisco, NYC, or London offices. ABOUT MERCOR 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. About the Role 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. What You'll Do - 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 What Makes This Role Different - You are building the core infrastructure that sits directly between customer requests and the outputs that ship — not internal tooling, not a support system - This product area is young and strategically central; early engineers are deciding the architecture, not inheriting it - The inputs are non-deterministic by nature — you are building durable orchestration over humans and models that can disagree with each other on hour 40 of a multi-stage job Day-to-Day - 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 What We're Looking For - 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; experience with Temporal or similar workflow engines is a plus You're likely someone who: - Gets frustrated by systems that are hard to debug and takes that personally enough to fix it - Has strong opinions about where state should live and can defend them in a design review - Moves fast but doesn't treat reliability as someone else's problem - Wants your work to have a short, visible line to outcomes that actually matter to customers Benefits - Bi-annual performance bonus structure - Generous equity grant vested over 4 years - Up to $15k Relocation bonus - $10K housing bonus (if you live within 0.5 miles of our office) - $1.5K monthly stipend for meals - Free Equinox membership - $200 monthly laundry reimbursement - $200 monthly personal wellness reimbursement - Health, Dental, Vision insurance

Requirements

Must-haves

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
What Makes This Role Different
You are building the core infrastructure that sits directly between customer requests and the outputs that ship — not internal tooling, not a support system
This product area is young and strategically central; early engineers are deciding the architecture, not inheriting it
The inputs are non-deterministic by nature — you are building durable orchestration over humans and models that can disagree with each other on hour 40 of a multi-stage job
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
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
Gets frustrated by systems that are hard to debug and takes that personally enough to fix it
Has strong opinions about where state should live and can defend them in a design review
Moves fast but doesn't treat reliability as someone else's problem
Wants your work to have a short, visible line to outcomes that actually matter to customers

Nice-to-haves

Comfort working in Python on AWS with Postgres; experience with Temporal or similar workflow engines is a plus

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlpythonawspostgres

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

Wellness