Tech Lead, Systems & Platform Applied AI

The Applied AI org builds the systems that turn human expertise into training data for frontier models, task pipelines, expert workflows, evaluation infrastructure, and the services that tie them together. As a Tech Lead for the Applied AI Backend Systems, you'll own services in that stack: designing the data models, building the APIs, services, and data pipelines that move work through the platform. This is a build role: take a roughly scoped, ambiguous problem, make the design calls, ship it to production, own it afterward, and mentor and grow other engineers on the team. Mercor works in-person five days a week in its San Francisco, NYC, or London offices.

Responsibilities

Own the architecture of the Applied AI backend domain, including core services, data models, orchestration systems, and the pipeline execution layer; Set the technical direction and stay hands-on enough to build the hardest parts; Take undefined problems, decide what is worth building, 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, backpressure), add failure recovery, profile hot spots, attribute agent token and cost usage, and set latency, error, and cost budgets; Own the design review bar for backend work across the org; Mentor senior engineers and make technical tradeoffs legible to leadership in writing; Provision and manage infrastructure as code using Terraform, and manage and launch tens of thousands of containers and sandbox environments; Participate in on-call for owned systems, debug production incidents, and write RCCAs; Drive cross-functional (XFN) alignment with product, operations, and research partners

Requirements

8+ years of professional backend engineering experience building and operating production systems; Track record of owning architecture across multiple teams and making decisions that aged well; Experience mentoring senior engineers, not just junior ones; Strong fundamentals in backend engineering: data structures, algorithms, concurrency, and clear code; Hands-on experience with API design (REST, gRPC, or GraphQL), including versioning, contracts, and backward compatibility; Solid database skills: relational data modeling, indexing, query performance, transactions and isolation, and safe migrations; Familiarity with at least one NoSQL or key-value store; Deep, hands-on expertise in distributed systems: queues and event streams, caching, idempotency, rate limiting, and designing for partial failure; Experience with data orchestration and workflow management systems such as Airflow, Temporal, or Dagster; Ability to debug lower-level infrastructure issues (containers, permissions, logs, traces); Experience running services in production: containers, CI/CD, monitoring and alerting, and debugging under real traffic; Comfort with ambiguity; Fluency with modern AI dev tools (e.g. Claude Code, Cursor, Copilot); Clear written and verbal communication; High ownership, pragmatism, and a bias toward shipping

Nice to have

Experience building or integrating with LLM-backed services in production (evaluation, orchestration, or serving); Familiarity with AI infrastructure providers such as Modal, Fireworks, Baseten, Temporal

Benefits

Bi-annual performance bonus; Generous equity grant vested over 4 years; Up to $15k relocation bonus; $10K housing bonus (if you live within 0.5 miles of the office); $1.5K monthly stipend for meals; Free Equinox membership; $200 monthly laundry reimbursement; $200 monthly personal wellness reimbursement; Health, dental, and vision insurance