Senior Staff Engineer – AI Data Path

🕒 Maio 6

🇺🇸 Estados Unidos – Remoto (EUA)

⏰ Tempo Integral

🟠 Sênior

🧑‍💻 Engenheiro Full-stack

🦅 Patrocina Visto H1B

Esta empresa já patrocinou vistos H1B no passado.

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DDN

SiteLinkedInTodas as Vagas

1001 - 5000 funcionários

Fundada em 1998

🤖 Inteligência Artificial

💰 $10.000.000 Funding Round em 2011-06

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

DDN é líder global em soluções de inteligência de dados para IA, fornecendo tecnologias de computação de alto desempenho (HPC) e gestão sofisticada de dados. Com foco em acelerar implantações de IA e análises avançadas de dados, os produtos da DDN, incluindo a Data Intelligence Platform e sistemas de storage avançados, atendem a setores diversos como saúde, serviços financeiros e governo. A DDN está comprometida em transformar a infraestrutura de dados das empresas para aproveitar todo o potencial da IA e impulsionar a eficiência operacional.

Descrição

• Lead the design and implementation of high-performance data movement pipelines using NVIDIA NIXL across GPU, CPU, and storage tiers • Architect and drive integration of DDN Infinia with GPU-accelerated inference platforms for large-scale, real-time AI workloads • Own end-to-end optimization of I/O paths between GPU memory and storage using technologies such as NVIDIA GPUDirect Storage, RDMA, and NVMe-over-Fabrics • Define and implement multi-tier storage architectures (NVMe, SSD, object storage) optimized for inference latency, throughput, and scalability • Lead development of advanced KV cache management strategies, including offloading, prefetching, and persistence across distributed storage layers • Partner with AI/ML engineering teams to optimize inference performance in frameworks such as PyTorch and TensorFlow • Establish benchmarking frameworks and lead performance tuning efforts for storage and data movement in production inference environments • Diagnose and resolve complex system bottlenecks across storage, networking, and GPU subsystems • Influence architecture decisions for distributed inference systems, ensuring scalability, resilience, and efficient data locality • Drive engineering excellence through best practices in observability, performance monitoring, automation, and reliability engineering • Mentor junior engineers and provide technical leadership across cross-functional teams

🎯 Requisitos

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field • 12+ years of experience in storage systems, distributed systems, or performance engineering • Proven track record of architecting and delivering large-scale, high-performance infrastructure systems • Deep expertise in distributed storage architectures (object storage, scalable file systems, or cloud-native storage platforms) • Strong understanding of Linux I/O stack, filesystem internals, and storage protocols • Extensive hands-on experience with NVMe, SSD optimization, and high-performance storage environments • Strong experience with RDMA, InfiniBand, or other high-speed data transfer technologies • Solid understanding of GPU computing concepts and CPU–GPU data movement patterns • Proficiency in Python and/or C/C++, with advanced debugging, profiling, and performance tuning skills • Demonstrated ability to optimize latency-sensitive, high-throughput production systems.

🏖️ Benefícios

• Dynamic and driven team structure • Engineering excellence opportunities • Mentoring of junior engineers • Opportunity for strong prioritization skills development • Hands-on involvement across various areas