Member of Technical Staff, Tech Lead Applied AI Backend
San Francisco • FullTime
Posted 18h ago
About the job
Mercor is a leading AI data company that organizes human intelligence to power the AI economy. We are building the layer between human expertise and frontier models, enabling millions of domain experts to train AI models. Our Applied AI organization develops systems that transform human expertise into training data for frontier models, task pipelines, expert workflows, and evaluation infrastructure. These systems run on reliable, fast, and observable backend infrastructure that scales with growing volume.
As a Tech Lead for Applied AI Backend Systems, you will own critical services within this stack, focusing on designing data models, building APIs and services, and developing data pipelines. This is a hands-on role where you will take roughly scoped, ambiguous problems, make design decisions, ship to production, and provide ongoing ownership. You will also mentor other engineers on the team, fostering their growth and development.
Responsibilities
- Own the architecture of the Applied AI backend domain, including core services, data models, orchestration systems, and pipeline execution layers.
- Set technical direction and remain hands-on to build the most challenging components.
- Take undefined problems, determine the scope of what to build, and own the outcome from design to production.
- Build and tune high-throughput data and job pipelines, focusing on queuing, batching, idempotency, retries, and backpressure.
- Enhance system speed and reliability by implementing failure recovery, profiling performance bottlenecks, and attributing agent tokens and costs.
- Set and adhere to latency, error, and cost budgets.
- Oversee the design review process for backend work across the organization.
- Mentor senior engineers, clearly communicate technical trade-offs to leadership, and elevate building standards.
- Provision and manage infrastructure as code using Terraform at scale, including launching and managing containers, sandbox environments, and resource allocation.
- Participate in on-call rotations, debug production incidents, and document learnings through RCCA.
- Drive cross-functional alignment through technical judgment and collaborate with product, operations, and research partners to translate ambiguous requirements into functional systems.
Requirements
- 8+ years of professional backend engineering experience building and operating production systems.
- Proven track record of owning architecture across multiple teams and making decisions with long-term viability.
- Experience mentoring senior engineers.
- Strong backend engineering fundamentals, including data structures, algorithms, and concurrency.
- Proficiency in writing clear, maintainable 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, isolation, and safe migrations.
- Familiarity with at least one NoSQL or key-value store and its appropriate use cases.
- Deep, hands-on expertise in distributed systems, including queues, event streams, caching, idempotency, rate limiting, and designing for partial failure.
- Experience with data orchestration and workflow management systems like Airflow, Temporal, or Dagster.
- Ability to troubleshoot lower-level infrastructure issues, including containers, permissions, logs, and traces.
- Experience running services in production, including containers, CI/CD, monitoring, alerting, and debugging under real traffic.
- Comfort with ambiguity, ability to ask clarifying questions, and develop actionable plans.
- Genuine excitement for agentic development and modern AI dev tools (e.g., Claude Code, Cursor, Copilot).
- Clear written and verbal communication skills.
- High ownership, pragmatism, and a bias toward shipping.
- Experience building or integrating with LLM-backed services in production (evaluation, orchestration, or serving) is a plus.
- Familiarity with AI infrastructure providers like Modal, Fireworks, Baseten, or Temporal is a plus.
Benefits
- Bi-annual performance bonus structure
- Generous equity grant vested over 4 years
- Up to $15k Relocation bonus
- $10K housing bonus (if you live within [details incomplete in raw text])