Software Engineer, Applied AI - Environments Platform

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

Frontier labs train and evaluate their models inside environments Mercor builds: sandboxed codebases, enterprise applications, multi-tool agent worlds, and the verifiers that score what an agent did. The Environments Infrastructure team owns the platform that builds those environments, runs them at scale, and gets them to the customer, however a given customer wants to consume them, from a service they call directly to a dataset they run in their own stack.

That makes this team the layer between everything Mercor authors and everyone who runs it. We build the core environment runtime, the structure of the environments themselves, the services that other teams at Mercor build on, the integrations our lab customers build against, and the pipelines that carry an environment from authoring to a training run. Every environment Mercor ships goes through this stack.

The team is small, the surface area is large, and most of the big design questions are still open. You will own real pieces of this platform end to end and see what you build in use at the biggest AI labs within weeks of shipping.

• Own the pipelines that take an environment from authoring to a lab's hands, including the packaging, validation, and versioning that make a delivery trustworthy.

• Design, build, and operate the core environment runtime: how environments are built, isolated, launched, and torn down across tens of thousands of concurrent sandboxes.

• Build the services other teams at Mercor use to run environments, and the APIs and integrations our lab customers build against.

• Build and extend the harnesses that run agents inside our environments, and keep results consistent between our stack and a lab's.

• Own features end to end: scope the problem, write the design, ship it, instrument it, and keep it healthy in production.

• Work on modern cutting edge tools, libraries and frameworks.

• Build for scale and reliability in the pipelines and job systems that move work through the platform: queuing, batching, retries, backpressure, and recovery when things break.

• Participate in on-call for the systems you own and write up what you learn in RCCA.

• Work directly with product, operations, and research partners to turn ambiguous requirements into systems that ship.

• 3+ years building and operating production backend or infrastructure systems.

• Strong fundamentals: data structures, concurrency, distributed systems, and quality code contributions.

• Hands-on API design, with an understanding of versioning and backward compatibility.

• Solid relational database skills: data modeling, query performance, and safe migrations.

• Deep comfort with containers and Linux: image builds, isolation, networking, and debugging at scale.

• Experience running workloads on Kubernetes or a serverless container platform like Modal.

• Experience running services in production: containers, CI/CD, monitoring and alerting, and debugging issues under real traffic.

• Clear written and verbal communication. High ownership, pragmatism, and a bias toward shipping.

• Experience with agent harnesses, RL environments, or evaluation infrastructure.

• Experience with workflow orchestration systems like Temporal, Airflow, or Dagster.

• Experience building or integrating with LLM-backed services in production (evaluation, orchestration, or serving).

• Familiarity with sandboxing and microVM runtimes such as gVisor, Firecracker, or Kata.

• Terraform or other infrastructure as code, and production experience on more than one cloud.

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 0.5 miles of our office)

• $1.5K monthly stipend for meals

• Free Equinox membership

• $200 monthly laundry reimbursement

• $200 monthly personal wellness reimbursement

• Health, Dental, Vision insurance