Sr. AI/ML Platform Engineer - CA, Santa Clara, United States

Originally posted 8 July 2026 by the employer.

ROCm/HIP

About the role

Build the platform layer that makes AI-for-engineering workflows scalable, reliable, and reproducible. Focus on the infrastructure supporting large-scale agent execution, distributed training and inference, experiment tracking, and GPU cluster utilization across the Blueprint framework.

What you'll do

- Build and operate the shared AI platform for agentic engineering workflows, including job submission, scheduling, orchestration, retries, logging, artifact storage, and experiment tracking. - Develop reliable infrastructure for distributed training, distributed inference, batch evaluation, and large-scale agent rollout across GPU clusters. - Support scalable integrations with compilers, ROCm/HIP tooling, profilers, simulators, EDA tools, vLLM, SGLang, and internal engineering systems. - Improve GPU cluster utilization, scheduling efficiency, reliability, quota management, and workload isolation. - Maintain artifact systems for generated kernels, RTL edits, traces, logs, profiler outputs, benchmark results, simulator outputs, and formal verification artifacts. - Partner with Applied AI Engineers to make Blueprint harnesses reusable across kernel optimization, RTL optimization, verification, firmware, and CPU/GPU performance workflows.

What you'll need

- Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or related field, or equivalent practical experience. - Strong programming skills in Python and one or more systems languages such as C++, Go, or Rust. - Experience building ML platforms, AI infrastructure, distributed systems, workflow orchestration, experiment platforms, GPU cluster infrastructure, or developer platforms. - Strong understanding of job scheduling, distributed workloads, logging, monitoring, reliability, storage systems, and production operations. - Experience with Kubernetes, Ray, Slurm, workflow engines, containerization, CI/CD, data pipelines, or large-scale compute orchestration. - Strong debugging skills across distributed systems, GPU workloads, storage systems, networking, containers, and production infrastructure.

Nice to have

- Master's or PhD degree with work in ML systems, reinforcement learning, distributed systems, GPU computing, or AI infrastructure. - Experience with GPU platforms, ROCm/HIP, CUDA, profiling, kernel benchmarking, model serving, or distributed training/inference. - Experience with vLLM, SGLang, Triton, PyTorch, JAX, Ray, Kubernetes, Slurm, MLflow, Weights & Biases, or similar systems. - Experience supporting LLM agents, tool-use systems, large-scale sampling, or automated program optimization workflows. - Familiarity with compiler, profiler, simulator, formal verification, EDA, firmware, or hardware performance workflows. - Experience operating shared GPU clusters or high-performance ML infrastructure in a multi-user research environment.

Skills: AI / ML Platform, GPU cluster utilization, distributed training, experiment management, ROCm/HIP, hardware domain experts