Sr. Technology Enablement Engineer

KLA•Ann Arbor, MI

2d•$129,600 - $190,067•Onsite

About The Position

In this role, you will play a key part in advancing business priorities by delivering high-impact work across your area of expertise. We are seeking a highly skilled Sr. Software Engineer with specialized expertise to design, build, and operate production-grade AI infrastructure for large-scale GPU training and inference. The role owns cluster architecture from ground zero, distributed workload orchestration, accelerator management, performance engineering, and open-source integration. TPU experience is optional.

Requirements

Bachelor's Degree and eight (8) years of Software Engineering experience
Four (4) years in software, cloud, platform, HPC, or systems engineering, including two (2) years supporting distributed AI/ML workloads.
Proven hands-on experience building GPU clusters from the ground up and operating them at production scale.
Deep Kubernetes expertise, including operators, CRDs, Helm, networking, storage, scheduling, and cluster lifecycle management.
Production experience with Ray and at least two of the following: NVIDIA NeMo RL, vLLM, SGLang, or NVIDIA Dynamo.
Strong knowledge of NVIDIA GPUs, CUDA, NCCL, GPU Operator, DCGM, MIG, RDMA, and multi-node collective communication.
Experience debugging training and inference performance across GPU, CPU, memory, network, and storage layers.
Proficiency in Python and Linux, plus experience with containers, CI/CD, GitOps, infrastructure-as-code, and platform observability.
Demonstrated open-source contribution, maintainership, or meaningful participation in an AI infrastructure project.

Nice To Haves

Experience with Google TPUs and TPU-oriented frameworks or distributed workloads.
Experience with large language model training, fine-tuning, RLHF or agentic reinforcement learning.
Knowledge of TensorRT-LLM, Triton Inference Server, DeepSpeed, Megatron-LM, or similar performance-oriented frameworks.
Experience operating secure, multi-tenant AI platforms in enterprise or regulated environments.

Responsibilities

Design and deploy scalable, multi-node GPU clusters on Kubernetes, including compute, networking, storage, scheduling, security, and observability.
Build distributed training and reinforcement learning platforms using Ray and NVIDIA NeMo RL, supporting frameworks such as PyTorch and JAX.
Deploy and optimize high-throughput LLM inference using vLLM, SGLang, and NVIDIA Dynamo.
Implement GPU scheduling, quotas, isolation, autoscaling, health monitoring, and capacity management for multi-tenant environments.
Profile and troubleshoot GPU workloads using NVIDIA Nsight Systems, Nsight Compute, DCGM, CUDA, NCCL, and related diagnostics.
Automate cluster provisioning, upgrades, workload deployment, and operational recovery through infrastructure-as-code and GitOps practices.
Contribute to or actively participate in relevant open-source AI infrastructure communities and bring upstream best practices into the platform.