Mercor
Software Engineer, Systems & Platform Applied AI
San Francisco · Mid
Sponsorship not specifiedDetected 22 days ago
Distributed SystemsBackend DevelopmentData StructuresAlgorithmsCode ReviewNoSQLCI/CDTemporalAPI DevelopmentGraphQLRESTgRPCAirflowData EngineeringLLMsControlsResearch
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Cap-exempt (no lottery)0
Sponsors this role90
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
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About the role
- We have synthetic pipelines and modular quality control systems that run in unison to generate highest quality tasks and at scale.
- All of it runs on backend systems that have to stay correct, fast, and observable while the volume behind them grows every month.
- We're hiring for depth in backend fundamentals rather than any particular domain.
Responsibilities
- Design, build, and operate backend services in the Applied AI stack including APIs, data models, background jobs, and the pipelines that connect them.
- Own features end to end: scope the problem, write the design, ship the code, instrument it, and keep it healthy in production.
- Build and tune high-throughput data and job pipelines: queuing, batching, idempotency, retries, and backpressure.
- Manage infrastructure as code using Terraform.
- Participate in on-call for the systems you own, debug production incidents, and write up what you learn in RCCA.
- Write clear design docs and give useful code review, we make technical decisions in writing and expect everyone to take part.
- Work directly with product, operations, and research partners to turn ambiguous requirements into systems that ship.
- 2-5 years of professional backend engineering experience building and operating production systems.
Requirements
- Hands-on experience with API design
- Hands-on experience with API design - REST, gRPC, or GraphQL, and an understanding of versioning, contracts, and backward compatibility.
Skills
- relational data modeling, indexing, query performance, transactions and isolation, and safe migrations.
- Familiarity with at least one NoSQL or key-value store and when it's the right choice.
- Experience with Data Orchestration and workflow management systems like Airflow, Temporal, Dagster.
- Experience running services in production: containers, CI/CD, monitoring and alerting, and debugging issues under real traffic.
- Clear written and verbal communication. High ownership, pragmatism, and a bias toward shipping.
- Familiarity with AI infrastructure providers like Modal, Fireworks, Baseten, Temporal
- 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
Compensation
- Bi-annual performance bonus structure
Benefits
- $200 monthly laundry reimbursement
- $200 monthly personal wellness reimbursement
- Health, Dental, Vision insurance
- Manage and launch 10s of 1000s of containers, sandbox environments, manage resource allocation and system health.
Company info
- 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.
This listing is sourced directly from Mercor's careers page and normalized into a canonical job model.