Senior Software Engineer, AI Frameworks

NVIDIA is a technology company seeking a Senior Software Engineer to integrate its Grove project with Dynamo and leading open-source AI frameworks. The role involves developing production-grade integrations, adapters, tooling, and reference implementations while improving performance, scalability, observability, and reliability across distributed AI training and inference environments.

Responsibilities:

• Design and implement end-to-end integrations of Grove with open-source AI frameworks (e.g., Dynamo, llm-d, Ray, PyTorch, and related ecosystem projects)
• Build and maintain adapters, plugins, operators, and/or runtime components that enable Grove features to work smoothly across training and inference stacks
• Partner with framework owners to upstream changes, contribute patches, and ensure long-term maintainability of integrations
• Develop reference workflows, sample apps, and best-practice guides that accelerate adoption by users and partners
• Optimize performance, scalability, and reliability for distributed training/inference, including multi-node and multi-GPU environments
• Improve observability and operational readiness (metrics, logging, tracing, debugging tools) for Kubernetes-based deployments
• Participate in technical design reviews, define APIs/contracts, and ensure compatibility across versions of frameworks and dependencies
• Diagnose complex issues spanning containers, networking, scheduling, CUDA/GPU utilization, and framework runtime behavior

Requirements:

• BS/MS/PhD in Computer Science, Electrical Engineering, or related field (or equivalent experience)
• 5+ years of proven experience in related field
• Hands-on experience integrating with at least one major AI framework/runtime (e.g., PyTorch, Ray, Triton Inference Server ecosystem, distributed runtimes, model serving stacks)
• Solid understanding of AI workloads: model development basics, training vs. inference tradeoffs, and performance considerations (throughput/latency, batching, memory)
• Experience with distributed systems concepts (RPC, scheduling, fault tolerance, resource management)
• Practical Kubernetes experience: deploying and operating services/jobs, Helm/Kustomize, operators/controllers (nice to have), and debugging clusters
• Familiarity with containers and cloud-native tooling (Docker, container registries, CI/CD pipelines)
• Strong software engineering experience in Go, C++ and/or Python, with a track record of shipping reliable systems
• Strong interpersonal skills and ability to collaborate across teams and with open-source communities
• Exceptional collaboration, communication, and documentation habits
• Open-source contributions to Dynamo, PyTorch, Ray, llm-d, Kubernetes ecosystem, or related ML infrastructure projects
• Experience with large-scale model serving, distributed inference, or multi-tenant AI platforms
• Experience building SDKs/APIs or developer tooling that improves integration usability
• Knowledge of GPU performance profiling and optimization (Nsight tools or similar), and/or kernel-level performance tuning
• Experience with reproducibility, packaging, versioning, and compatibility testing across fast-moving dependencies