Senior Software Engineer, Infrastructure Software AI; Centralized AI Data Centers & Distrib

Senior Software Engineer, Infrastructure Software for AI (Centralized AI Data Centers & Distributed AI-RAN Environments)

We challenge conventional limits by building transformative products that fully exploit our state-of-the-art (SOTA) infrastructure—including NVIDIA GB200 (e.g., liquid‑cooled NVL
72 rack‑scale systems with 72 Blackwell GPUs and 36 Grace CPUs unified via massive NVLink domains), MGX modular architectures, and DGX Grace Hopper platforms—combined with cloud‑native software. These solutions power massive centralized AI data centers and pioneering distributed AI Radio Access Network (AI‑RAN) deployments, where AI is embedded directly into radio networks for superior efficiency, edge intelligence, and new capabilities.

We're seeking experienced senior engineers passionate about innovation, ready to shape foundational AI infrastructure software on world‑class GPU systems.

• Bachelor's degree in Computer Science, Electrical Engineering, or a related technical field.
• 5+ years of experience in software engineering, hardware platforms, distributed systems, or infrastructure development.
• 2+ years in technical lead roles, owning high‑impact projects and leading teams.
• Proven hands‑on experience building systems software, AI frameworks, or applied AI systems.

• Master's degree in a relevant technical discipline (e.g., CS, Systems Engineering).
• Direct experience with Kubernetes and container orchestration at scale.
• Hands‑on work with GPU‑accelerated systems and high‑performance computing (HPC) environments.
• Familiarity with AI developer frameworks, MLOps tools, automation pipelines, and CI/CD systems.

Join our infrastructure team as a key contributor building foundational systems software atop next‑generation GPU platforms to support large‑scale AI workloads (training, fine‑tuning, and serving). Own significant portions of our new AI infrastructure stack, with a strong emphasis on Kubernetes orchestration and GPU resource management. Drive architectural innovation in systems software and automation to achieve maximum efficiency and utilization. As a Senior Software Engineer, collaborate closely with Engineering Leads, Product Management, and Program teams to execute toward commercialization and market impact.

• Design and develop robust systems software to enable AI workloads on large‑scale GPU clusters (e.g., GB200 NVL
  72 rack‑scale systems).
• Deliver critical control plane components for workload scheduling, orchestration, and resource management; build management plane for underlying hardware platforms.
• Create northbound APIs and interfaces for customer portals and self‑service access to the infrastructure.
• Contribute to product requirements documents (PRDs), sprint planning, and agile program execution.
• Help attract, mentor, and grow top engineering talent.
• Exemplify and cultivate a culture of humility, bold innovation, and disciplined delivery to bring products to market.

We are pushing the boundaries of AI by challenging conventional approaches and building groundbreaking products that fully harness our state‑of‑the‑art (SOTA) infrastructure—including NVIDIA GB200 NVL
72 (liquid‑cooled, 72‑GPU rack‑scale systems with massive NVLink domains for exascale performance), MGX modular architectures, and DGX Grace Hopper platforms. These products power both massive centralized AI data centers and innovative distributed AI Radio Access Network (AI‑RAN) deployments, where AI is natively integrated into radio networks for enhanced efficiency, edge intelligence, and new revenue opportunities.

We're seeking passionate, experienced practitioners eager to drive innovation and deliver transformative AI products on world‑class hardware.

• Bachelor's degree in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related technical field.
• 3+ years of hands‑on experience in machine learning, deep learning, and software engineering.
• Proficiency in Python; experience with C/C++.
• Strong working knowledge of major AI/ML frameworks (PyTorch, Tensor Flow, JAX, or similar).
• Solid foundation in data structures,…