AI Infrastructure Engineer

About the role

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.

Summary of role:

All of Mercor’s AI work, from training and serving models to building agents, runs on GPUs. Your job is to make that compute fast, fault-tolerant and fully utilized.

You’ll own the accelerator platform end to end. That means the clusters and schedulers across AWS, GCP, neoclouds and on-prem, the training and LLM serving on top, and the observability and cost data that show where every GPU-hour goes.

This is a software engineering role, not an ops role. You’ll write production code every day, own systems from design through on-call, and build the platform that sets how fast Mercor can move.

Representative projects

- Stand up a multi-cloud GPU fleet spanning hyperscalers, neoclouds and on-prem, orchestrated through custom Kubernetes and Slurm operators so researchers never have to think about where their job lands.

- Build a scheduling layer with gang scheduling, bin-packing and fair-share quotas that measurably raises accelerator utilization across teams.

- Ship a high-throughput vLLM serving stack with prefill/decode disaggregation, KV cache offload and FP8/FP4 quantization. Back it with a benchmarking and traffic-replay harness that tracks TTFT, TPOT and latency percentiles against per-config baselines.

- Build a self-healing health-check and remediation system that rides out fault storms across hardware, network and software without paging a human.

- Build an end-to-end cost-attribution pipeline. It ingests utilization, occupancy and billing data into the warehouse, attributes every GPU-hour to a team and workload, and drives capacity planning.

- Build internal tools and dashboards that researchers and non-engineers use every day.

Responsibilities:

- Design, deploy and operate GPU clusters across AWS, GCP, and other hyperscalers/neoclouds/on-premise data centers, including custom Kubernetes and Slurm operators and the metadata services behind them.