5 days ago
Base Salary
$250k - $500k/yr
Responsibilities
- Own the architecture of Applied AI backend services, data models, orchestration systems, and pipeline execution layers.
- Set technical direction while remaining hands-on in designing and building complex backend systems.
- Build and optimize high-throughput data and job pipelines using queuing, batching, idempotency, retries, backpressure, caching, and failure recovery.
- Define and enforce latency, error, and cost budgets, including agent token and cost attribution.
- Lead backend design reviews, mentor senior engineers, document technical tradeoffs, and improve engineering standards.
- Provision and manage infrastructure as code with Terraform, including large-scale container and sandbox environments.
- Participate in on-call, debug production incidents, and document root-cause analysis.
- Drive cross-functional alignment and translate ambiguous requirements into production systems.
Requirements
- 8+ years of professional backend engineering experience building and operating production systems.
- Experience owning architecture across multiple teams and making durable technical decisions.
- Experience mentoring senior engineers.
- Strong backend 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 relational database skills covering data modeling, indexing, query performance, transactions, isolation, and safe migrations.
- Familiarity with at least one NoSQL or key-value store and knowledge of when to use it.
- Deep hands-on distributed-systems experience with queues, event streams, caching, idempotency, rate limiting, and partial-failure design.
- Experience with workflow and data orchestration systems such as Airflow, Temporal, or Dagster.
- Ability to investigate lower-level infrastructure issues involving containers, permissions, logs, and traces.
- Experience running production services with containers, CI/CD, monitoring, alerting, and real-traffic debugging.
- Comfort working through ambiguity with strong ownership, pragmatism, and a bias toward shipping.
- Fluency with modern AI development tools such as Claude Code, Cursor, or Copilot.
- Clear written and verbal communication skills.
- 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.
- 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.