Senior Benchmark and Performance Engineer – AI and Storage Systems

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August 28

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DDN

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Artificial Intelligence • Data Center and Cloud Computing • High Performance Computing

DDN is a global leader in AI data intelligence solutions, providing high-performance computing and sophisticated data management technologies. With a focus on accelerating AI deployments and advanced data analytics, DDN's products, including the Data Intelligence Platform and advanced storage systems, serve diverse sectors such as healthcare, financial services, and government. DDN is committed to transforming enterprise data infrastructure to leverage the full potential of AI and drive operational efficiency.

1001 - 5000 employees

Founded 1998

🤖 Artificial Intelligence

💰 $10M Funding Round on 2011-06

📋 Description

• Design and execute performance benchmarks across AI, HPC, and storage platforms • Run and tune AI inference workloads using frameworks such as PyTorch, TensorFlow, Triton, NVIDIA NIMs, and vector databases • Benchmark large-scale RAG pipelines including data ingestion, retrieval, and inference performance • Profile and optimize MPI and multi-node distributed applications • Compile and debug C/C++, Python, and CUDA-based codes across heterogeneous systems • Generate automated test scripts and benchmarking workflows (e.g., with Bash, Python, or Slurm job scripts) • Analyze and visualize results using Excel, Jupyter, or reporting tools; create comparison graphs and KPIs • Write clear, concise performance reports for both technical and non-technical stakeholders • Present findings internally and externally, translating results into architectural guidance for field engineers and sales teams • Collaborate with system engineers, product managers, and partners to tune and improve software/hardware stack performance • Validate and tune performance on storage systems including parallel file systems (e.g., Lustre, GPFS), object storage, and NVMe over Fabrics • Contribute to internal tooling to automate test cycles and performance regression tracking

🎯 Requirements

• 7+ years of experience in performance engineering, benchmarking, or HPC/AI systems • Deep experience with AI/ML and deep learning frameworks (PyTorch, TensorFlow, ONNX, Triton) • Familiarity with NVIDIA NIMs and containerized model serving stacks • Proven expertise with MPI, OpenMP, Slurm or similar schedulers in large-scale compute environments • Solid understanding of file and storage systems (e.g., POSIX, Lustre, S3, NVMe-oF) • Strong Linux skills (debugging, tuning, networking, storage stack) • Proficiency in scripting (e.g., Bash, Python) for job orchestration and result parsing • Ability to create clear Excel graphs and presentations from raw benchmark data • Strong communication skills — able to convey technical results and trade-offs to engineering and customer-facing teams • Preferred: Experience with RAG pipelines, vector databases (e.g., FAISS, Milvus, Qdrant) • Familiarity with Kubernetes and CSI-based persistent volume systems • Understanding of GPU profiling tools (Nsight, nvprof, PyTorch Profiler) • Knowledge of telemetry and monitoring frameworks (e.g., Prometheus, Grafana) • Prior work publishing or presenting technical performance results