AI Systems Performance Engineer

About the role

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Job Description:

Senior AI Network Performance Engineer (NIC / Host Networking)

We are seeking an experienced Senior AI Network Performance Engineer to join the Performance Lab within our Architecture group. You will benchmark AI inference, training, and storage workloads on GPU clusters, with a focus on NIC and host networking performance. Your reports will guide customers in deployment and help our marketing team position the product.

Your primary focus will be the server side of the network: the NIC, its drivers, and the host stack that connects AI workloads to the fabric. You will run rigorous benchmarks, isolate bottlenecks, and tune for maximum throughput and minimum latency. If you have gone deep on NIC performance, built or operated AI clusters, or run inference serving at scale, we want to hear from you.

Key Responsibilities

Benchmarking: Install, configure, and run AI performance benchmarks, including MLPerf (Training and Inference) and NCCL collective communication tests.

NIC and host tuning: Optimize NIC, driver, and host network settings (RDMA/RoCEv2, congestion control, queue and interrupt configuration, NUMA placement) for distributed AI workloads.

Debugging: Isolate performance bottlenecks across the Linux OS, server hardware, NICs, and the network.

Automation: Build tools and frameworks that make benchmarking repeatable.

Communication: Document test methodologies and present findings and recommendations to hardware, software, and networking teams.

Required Qualifications

Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field. We typically look for 12+ years of related industry experience, but candidates with fewer years and an exceptional track record are encouraged to apply.

Strong hands-on Linux experience, including system-level performance tuning and troubleshooting.

Proficiency in Python or a similar scripting language.

Deep experience in at least one of the following:

NIC / host networking: performance tuning or validation of high-speed NICs, drivers, or RDMA/RoCEv2.

AI cluster building: bringing up and tuning multi-node GPU clusters, including collective communication libraries such as NCCL.

Proven ability to diagnose root causes of performance problems in distributed systems.

Preferred Qualifications

Experience in more than one of the three areas above.

Experience with MLPerf Training or Inference.

Familiarity with Ethernet switch and fabric behavior (PFC, ECN, load balancing).

Experience with Docker, Kubernetes, or Slurm in AI deployments.