AI Infrastructure Engineer

Mercor

San Francisco (CA)

On-site

USD 180,000 - 280,000

Full time

4 days ago

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Benefits offered by this job

Bi-annual performance bonus structure

Generous equity grant vested over 4 yr

Up to $15k Relocation bonus

$10K housing bonus (0.5 miles of our o

$1.5K monthly stipend for meals

Free Equinox membership

$200 monthly laundry reimbursement

$200 monthly personal wellness reimb

Health, Dental, Vision insurance

Job summary

Mercor is hiring a software engineer to own the accelerator platform end-to-end. You will operate GPU clusters across AWS, GCP, neoclouds and on-prem data centers, with custom Kubernetes and Slurm operators.

The role focuses on design, deployment, and on-call ownership to keep accelerators busy and costs under control. You will build a high-throughput vLLM serving stack, implement auto-scaling, and create tooling for benchmarking, observability, and cost attribution.

Qualifications

5+ years building and operating production distributed systems or ML infrastructure.
Proficient in Python and at least one of Go or C++.
Deep Kubernetes experience and HPC tools (Slurm).
Experience running GPU workloads at scale and multi-cloud infra.

Responsibilities

Design, deploy and operate GPU clusters across AWS, GCP, hyperscalers, neoclouds and on-prem data centers, including custom Kubernetes and Slurm operators.
Build scheduling to maximize accelerator utilization with gang scheduling, bin-packing and quotas.
Run distributed training infrastructure using PyTorch Distributed, Ray, or Megatron.
Deploy and tune high-throughput LLM serving with vLLM, including quantization and cache strategies.
Create benchmarking and traffic-replay tools to measure latency and throughput and set baselines.
Implement health checks and automated remediation for fault storms without human intervention.
Instrument fleet utilization with DCGM, Prometheus and Grafana; write monitoring rules.
Ingest utilization and billing data, support capacity planning and autoscaling initiatives.
Prepare runbooks and internal docs; communicate tradeoffs to researchers, finance and leadership.

Skills

Distributed systems

Production systems

Python

C++

Kubernetes

Git/shell

GPU workloads

Tools

Kubernetes

Slurm

Prometheus

Grafana

DataDog

CUDA

Triton

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.

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.
Build scheduling that keeps accelerators busy: gang scheduling for distributed jobs, bin-packing to reduce fragmentation, and quotas and fair share for multi-tenant teams.
Run distributed training infrastructure on PyTorch Distributed, Ray, or Megatron, with fast checkpointing and model loading.
Deploy and tune high-throughput LLM serving with vLLM or similar: tensor and model/expert parallelism, prefill/decode disaggregation, KV cache offload, FP8/FP4 quantization.
Build benchmarking and traffic-replay tools that measure TTFT, TPOT and latency percentiles, and set per-config performance baselines.
Build health checks across hardware, network and software layers, and automated remediation that handles fault storms without human intervention.
Instrument fleet utilization with DCGM, Prometheus and Grafana. Write recording rules and alerts that teams rely on.
Ingest utilization, occupancy and billing data into a warehouse, attribute spend to teams and workloads, and support capacity planning.
Drive autoscaling, rightsizing and idle-capacity recovery programs with measurable cost results.
Write clear runbooks and documentation, and explain infrastructure tradeoffs to researchers, finance and leadership.
Build internal tools or dashboards that researchers and non-engineers use daily.

You may be a good fit if you

Have 5+ years building and operating production distributed systems or ML infrastructure
Write production-quality Python plus Go or C++, and are fluent in shell scripting for system administration.
Have deep hands-on Kubernetes and container experience, plus working knowledge of Slurm or another HPC resource manager.
Have run GPU workloads at scale, including scheduling, orchestration and troubleshooting.
Have hands-on experience with at least one distributed training or serving stack: PyTorch Distributed, Ray, vLLM or Megatron-LM.
Have deep experience with at least one major cloud provider.
Know observability tools like DataDog, Prometheus, PromQL and Grafana, and are comfortable on-call for critical systems.
Own projects end to end and gather your own requirements with little direction.

Strong candidates may also have experience with (or a strong desire to learn deeply)

LLM inference optimization: KV cache systems, disaggregated serving, quantization, speculative decoding
High-performance networking: RDMA, InfiniBand, NCCL, NVLink
GPU profiling with Nsight or similar, and CUDA or Triton kernel work
Accelerators beyond NVIDIA: AMD MI-series, TPU, Trainium
Capacity planning, billing normalization or cost attribution across providers
Automated fault diagnosis, log mining or AIOps at fleet scale
Configuration management (Ansible, Puppet) and secure or air-gapped deployments

Benefits