Senior Staff Engineer – AI Data Path

🕒 vor 1 Monat

🇺🇸 Vereinigte Staaten – Remote

⏰ Vollzeit

🟠 Senior

🧑‍💻 Full-Stack-Entwickler

🦅 H1B-Visum-Sponsor

Dieses Unternehmen hat in der Vergangenheit H1B-Visa gesponsert.

Jetzt Bewerben

Ähnliche Remote-Jobs finden

E-Mails für Remote-Jobs erhalten

📊 Überprüfen Sie Ihre Lebenslauf-Bewertung für diese Stelle

Verbessern Sie Ihre Chancen auf ein Vorstellungsgespräch, indem Sie Ihre Lebenslauf-Bewertung vor der Bewerbung überprüfen.

Lebenslauf hochladen

DDN

WebsiteLinkedInAlle Stellen

1001 - 5000 Mitarbeiter

Gegründet 1998

🤖 Künstliche Intelligenz

💰 €10.000.000 Funding Round im 2011-06

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

DDN ist ein globaler Marktführer für Lösungen im Bereich Datenintelligenz für KI und bietet Technologien für High-Performance Computing (HPC) sowie anspruchsvolles Datenmanagement. Mit dem Fokus, KI-Implementierungen und fortgeschrittene Datenanalysen zu beschleunigen, unterstützen die Produkte von DDN – darunter die Data Intelligence Platform und hochentwickelte Speichersysteme – vielfältige Branchen wie Gesundheitswesen, Finanzdienstleistungen und den öffentlichen Sektor. DDN hat sich der Transformation der Unternehmensdateninfrastruktur verschrieben, um das volle Potenzial von KI auszuschöpfen und die operative Effizienz zu steigern.

Beschreibung

• 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

🎯 Anforderungen

• 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.

🏖️ Vorteile

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