AI Infrastructure Engineer

Summary of Role

AI Infrastructure Engineer will build and run the GPU and accelerator platform our AI workloads depend on. That covers the clusters and schedulers underneath, training and inference serving on top, and the observability and cost data that tell us how well the fleet is used. The role will write production code every day and own systems end to end, from design through on-call.

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.

Skills Required

• 5+ years building and operating production distributed systems or ML infrastructure (7+ for Staff).
• Production-quality Python plus one of Go or C++.
• Shell scripting for system administration.
• Deep hands-on Kubernetes and containers. Working knowledge of Slurm or another HPC resource manager.
• Experience running GPU workloads at scale, including scheduling, orchestration and troubleshooting.
• Hands-on with at least one distributed training or serving stack: PyTorch Distributed, Ray, vLLM, Megatron-LM.
• Deep experience with at least one major cloud provider.
• Observability with tools such as DataDog, Prometheus, PromQL and Grafana. Comfortable on-call for critical systems.
• Owns projects end to end and gathers their own requirements with little direction.

Familiarity and/or willing to learn at a deep level the following

• LLM inference optimization: KV cache systems, disaggregated serving, quantization, speculative decoding.
• High-performance networking: RDMA, InfiniBand, NCCL, NVLink.
• GPU profiling with Nsight or similar. 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

• 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