Software Engineer, Frontier Data Products

Mercors mission is to organize human intelligence to power the AI economy. Were 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. Mercors APEX benchmark family measures AIs 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. Youll 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.

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 cant be captured in code alone. Today more than 30000 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. Youll 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 Youll 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 Were 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

Youre 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 doesnt treat reliability as someone elses 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

Required Experience:

IC