AI and HPC Systems Performance Engineer

Location: Hybrid, with an average requirement of two days per week from an HPE office

Company

hirify.global delivers edge-to-cloud infrastructure and high-performance computing solutions for complex, data-intensive workloads.

What you will do

• Install, configure, and optimize GPU servers, storage, high-speed networking, and AI software stacks on HPE platforms.
• Benchmark and characterize AI and machine learning training, inference, LLM, multimodal, RAG, and distributed workloads.
• Analyze telemetry, metrics, logs, traces, and profiling data to identify bottlenecks and improve performance and scalability.
• Develop automation scripts, deployment frameworks, infrastructure-as-code solutions, and observability tools for AI platforms.
• Collaborate with customers, partners, GPU vendors, and internal engineering teams to troubleshoot and optimize AI solutions.
• Evaluate emerging AI technologies, author technical reports and reference architectures, and provide technical leadership and mentoring.

Requirements

• Typically 8+ years of experience in engineering or related technical fields.
• Strong Linux system administration and command-line experience across enterprise distributions.
• Experience with AI/ML frameworks and workloads, including PyTorch, JAX, model training, inference, benchmarking, and optimization.
• Experience with GPU-accelerated systems, CUDA, NCCL, distributed GPU environments, and multi-node AI clusters.
• Experience with high-performance networking, including InfiniBand, RDMA, RoCE, and Mellanox/NVIDIA networking solutions.
• Proficiency in Python, Bash, Go, C++, or a similar programming or scripting language; experience with Docker, Kubernetes, profiling, tracing, and observability tools.

Nice to have

• MS, ME, MTech, or PhD in computer science, computer engineering, electrical engineering, data science, artificial intelligence, or a related discipline.
• Experience with HPE platforms, AI Factory architectures, enterprise AI infrastructure, and large-scale foundation model workloads.
• Experience with Weka, Lustre, BeeGFS, GPFS, MLPerf, or other high-performance storage and benchmarking technologies.
• Experience developing reference architectures, technical papers, benchmark studies, or performance guidance.

Culture & Benefits

• Hybrid work structure with flexibility to manage work and personal needs.
• Health and wellbeing benefits supporting employees and their families.
• Personal and professional development programs aligned with career goals.
• Inclusive environment that values varied backgrounds and individual uniqueness.
• Opportunities to work with globally distributed teams and technology partners.