5 days ago
Base Salary
$220k - $500k/yr
Responsibilities
- Design, build, and operate backend services, APIs, data models, background jobs, and data pipelines.
- Own features end to end from scoping and design through production launch, instrumentation, and ongoing operation.
- Build and tune high-throughput pipelines using queuing, batching, idempotency, retries, backpressure, caching, and failure recovery.
- Improve system performance and reliability through profiling, observability, metrics, logging, tracing, alerts, and latency, error, and cost budgets.
- Manage and launch large numbers of containers and sandbox environments, including resource allocation and system health.
- Manage infrastructure as code using Terraform.
- Participate in on-call rotations, debug production incidents, and document learnings in RCCA.
- Write design documents, review code, and collaborate with product, operations, and research partners.
- Work with modern tools, libraries, and frameworks to build systems for the Applied AI platform.
Requirements
- 2–5 years of professional backend engineering experience building and operating production systems.
- Strong fundamentals in data structures, algorithms, concurrency, and maintainable code.
- Hands-on API design experience with REST, gRPC, or GraphQL, including versioning, contracts, and backward compatibility.
- Strong database skills covering relational modeling, indexing, query performance, transactions, isolation, safe migrations, and familiarity with a NoSQL or key-value store.
- Practical distributed-systems experience with queues, event streams, caching, idempotency, rate limiting, and partial-failure handling.
- Experience with workflow and data orchestration systems such as Airflow, Temporal, or Dagster.
- Experience operating production services with containers, CI/CD, monitoring, alerting, and real-traffic debugging.
- Ability to investigate lower-level infrastructure issues involving containers, permissions, logs, and traces.
- Familiarity with modern AI development tools such as Claude Code, Cursor, or Copilot.
- Clear written and verbal communication, ownership, pragmatism, and comfort working through ambiguity.
- Preferred: experience building or integrating LLM-backed services in production for evaluation, orchestration, or serving.
- Preferred: familiarity with AI infrastructure providers such as Modal, Fireworks, Baseten, or Temporal.
Benefits
- Bi-annual performance bonus structure and equity grant.
- Relocation and housing support may be available.
- Monthly meal, laundry, and personal wellness reimbursements.
- Free Equinox membership.
- Health, dental, and vision insurance.
- In-person work five days per week in the San Francisco, New York City, or London office.
About Mercor
Mercor builds an expert-powered platform that trains, evaluates, and deploys AI systems for AI labs and enterprises. It operates APEX to assess model performance on economically valuable tasks, and provides services such as custom AI agents and teams of vetted domain experts who encode organizational knowledge into AI. Founded in 2023 and headquartered in San Francisco, it is privately held and works with frontier AI labs and enterprise clients requiring strict data isolation.