GPU & ML Infrastructure Engineer

GPU & ML Infrastructure Engineer

Experience: 10+ Years
Role: GPU & ML Infrastructure Engineer
Work Environment: Data Center & Edge GPU Platforms

Job Summary

We are looking for an experienced GPU & ML Infrastructure Engineer with 10+ years of expertise in NVIDIA GPU environments, Linux systems, infrastructure automation, and machine learning workloads.

The ideal candidate will be responsible for building, deploying, and maintaining infrastructure for GPU benchmarking, stress testing, telemetry collection, and ML workloads. The role requires strong hands-on experience with NVIDIA GPU platforms, Linux, automation, performance validation, and LLM workloads, along with the ability to deliver reliable and reproducible datasets.

Key Responsibilities

• Build, configure, and maintain infrastructure for NVIDIA GPU benchmarking, stress testing, and performance validation.
• Port existing test procedures and workloads to new GPU hardware and platforms.
• Automate infrastructure deployment, configuration, testing, and validation processes.
• Execute and evaluate LLM inference and machine learning training workloads across GPU platforms.
• Collect, validate, and analyze GPU and system telemetry to assess performance, reliability, and utilization.
• Perform data-quality checks and troubleshoot inconsistencies in telemetry and benchmark datasets.
• Develop and maintain automated scripts and tools for testing, monitoring, and infrastructure validation.
• Work with data-center and edge GPU platforms to ensure consistent and reproducible test environments.
• Investigate performance bottlenecks, hardware/software issues, and infrastructure failures.
• Generate and deliver reproducible datasets, benchmark results, and technical reports.
• Collaborate with hardware, software, ML, and infrastructure teams to improve GPU platform readiness and performance.

Required Skills

• 10+ years of experience in infrastructure engineering, GPU systems, or related domains.
• Strong hands-on experience with NVIDIA GPUs and GPU infrastructure.
• Excellent knowledge of Linux systems administration and troubleshooting.
• Experience with GPU benchmarking, stress testing, and performance analysis.
• Strong understanding of machine learning workloads, including LLM inference and training.
• Experience with infrastructure automation and scripting.
• Hands-on experience with GPU/system telemetry, monitoring, and data validation.
• Ability to troubleshoot hardware, driver, OS, and workload-level issues.
• Experience working with data-center and edge computing environments.
• Strong analytical and problem-solving skills.

Preferred Skills

• Experience with NVIDIA GPU tools and ecosystem.
• Experience with CUDA and GPU performance profiling.
• Knowledge of containerization technologies such as Docker and Kubernetes.
• Experience with Python, Bash, or similar scripting languages.
• Familiarity with ML frameworks such as PyTorch or TensorFlow.
• Experience building automated benchmarking or validation frameworks.
• Understanding of GPU drivers, firmware, networking, storage, and system-level performance.

Key Skills

NVIDIA GPU | GPU Infrastructure | Linux | CUDA | Machine Learning | LLM | GPU Benchmarking | Stress Testing | Telemetry | Performance Testing | Infrastructure Automation | Python | Bash | Docker | Kubernetes | Data Validation | GPU Performance