Member of Technical Staff, Tech Lead Applied AI Backend

mercor; San Francisco (CA); On-site; USD 170,000 - 250,000; Full time

Job summary

Mercor in San Francisco is seeking a Tech Lead for Applied AI Backend Systems to own architecture of backend services, data models, and pipelines, shipping designs to production and mentoring engineers. You’ll design data pipelines, build scalable APIs (REST/GRPC/GraphQL), manage infrastructure as code with Terraform, and drive cross-functional alignment with product, operations, and research teams.

Deep knowledge of distributed systems, databases, and reliability is required; leadership and the

Qualifications

• 8+ years of backend engineering experience in production systems.
• Experience mentoring senior engineers.
• Strong fundamentals in data structures, algorithms, concurrency and clear code.
• Hands-on API design and versioning (REST, gRPC, GraphQL).
• Deep distributed systems expertise: queues, caching, idempotency, rate limiting.

Responsibilities

• Own the architecture of the Applied AI backend domain, including core services, data models, and the pipeline execution layer.
• Set the technical direction and contribute hands-on to build the hardest parts.
• Translate undefined problems into concrete designs, write the design, ship the code, and keep it healthy in production.
• Drive cross-functional alignment with product, operations, and research partners to turn ambiguous requirements into shippable systems.
• Participate in on-call for owned services and improve the system reliability.

Skills

Backend architecture

Mentoring engineers

Distributed systems

APIs (REST/GRPC/GraphQL)

Data modeling

Tools

Terraform

Airflow

Temporal

Dagster

Job description

About Mercor

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.

About the Role

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. 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 reliable, fast, and observable while the volume behind them grows every month.

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, Data pipelines that move work through the platform. This is a build role and you'll take a problem that's roughly scoped, ambiguous, make the design calls, ship it to production, and own it afterward, mentor other engineers on the team and grow them.

We're hiring for depth in backend fundamentals rather than any particular domain. If you've built and operated real services, handled the schema migration that couldn't take downtime, found the query that fell over at 10x traffic, designed the retry logic that made a flaky dependency invisible to users, or build a system that recover when things break that's the experience that matters here. You'll work with a talent dense group of engineers who will review your designs and push your thinking.

What you will Do

• Own the architecture of the Applied AI backend domain; core services, data models, orchestration systems and the pipeline execution layer that the product and ops team in the org depends on.
• Set the technical direction, then stay hands-on enough to build the hardest parts yourself.
• You'll take problems that arrive undefined, decide what's worth building, and own the outcome - the scoping is part of the job, 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. Make the system fast and reliable by adding failure recovery in pipelines, profile hot spots, Agent token and cost attribution, caching issues, and set latency, error, cost budgets you actually hold to.
• Own the design review bar for backend work across the org. Mentor senior engineers, make the technical tradeoffs legible to leadership in writing, and raise the standard for how we build.
• Provision and manage infrastructure as code using Terraform and at scale. Manage and launch 10s of 1000s of containers, sandbox environments, manage resource allocation and system health.
• Participate in on-call for the systems you own, debug production incidents, and write up what you learn in RCCA.
• Drive XFN alignment across teams through technical judgment and work directly with product, operations, and research partners to turn ambiguous requirements into systems that ship.

What we are Looking For

• 8+ years of professional backend engineering experience building and operating production systems with a track record of owning architecture across multiple teams and of decisions that aged well.
• Experience mentoring senior engineers, not just junior ones.
• Strong fundamentals in backend engineering: data structures, algorithms, concurrency, and writing code that's clear enough for the next person to change.
• Hands-on experience with API design - REST, gRPC, or GraphQL, and an understanding of 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 and when it's the right choice.
• 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 like Airflow, Temporal, Dagster.
• Be able to roll your sleeves up and dig deeper into the lower level infrastructure