We are seeking a Senior Software Engineer to drive integration of the NVIDIA Grove project within Dynamo and across a set of leading open-source AI frameworks. In this role, you will develop production-grade software enabling Grove capabilities to be adopted, scaled, and operated smoothly. In this role, you will build production-grade software that enables seamless adoption, scaling, and operation of Grove capabilities across environments such as Dynamo, llm-d, Ray, PyTorch, and other emerging frameworks in the AI ecosystem. You will collaborate across engineering teams and the open-source community to deliver robust integrations, reference implementations, and developer-focused tooling.
What you'll be doing:
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.
What we need to see:
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.
Ways to stand out from the crowd:
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.
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most experienced and hard-working people in the world working for us. Are you creative and autonomous? Do you love a challenge? If so, we want to hear from you
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- BS, MS, or PhD in Computer Science, Electrical Engineering, or a related field, or equivalent experience
- 5+ years of proven experience in a related field
- Hands-on experience integrating with at least one major AI framework or runtime, such as PyTorch, Ray, Triton Inference Server, distributed runtimes, or model serving stacks
- Understanding of AI workloads, including model development basics, training and inference tradeoffs, throughput, latency, batching, and memory
- Experience with distributed systems concepts including RPC, scheduling, fault tolerance, and resource management
- Practical Kubernetes experience deploying and operating services or jobs and debugging clusters
- Familiarity with containers and cloud-native tooling, including Docker, container registries, and CI/CD pipelines
- Strong software engineering experience in Go, C++, and/or Python with a record of shipping reliable systems
- Strong interpersonal, collaboration, communication, and documentation skills
- Open-source contributions to Dynamo, PyTorch, Ray, llm-d, Kubernetes, or related machine-learning infrastructure projects
- Experience with large-scale model serving, distributed inference, or multi-tenant AI platforms
- Experience building SDKs, APIs, or developer tooling
- Knowledge of GPU performance profiling and optimization using Nsight tools or similar, or kernel-level performance tuning
- Experience with reproducibility, packaging, versioning, and compatibility testing across fast-moving dependencies
- Experience with Kubernetes operators or controllers
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Company stock and RSUs have driven exceptional total compensation, with a generous employee stock purchase plan and ongoing equity refreshers playing a central role. Feedback suggests historic share-price gains created significant financial upside for many.
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Healthcare Strength — Multiple plan options (HSA, PPO, and in some locations Kaiser) with in-network preventive care covered at 100% and company HSA contributions indicate a robust offering. Mental-health sessions and broad dental, vision, and disability coverage further strengthen the package.
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Parental & Family Support — Up to 22 weeks of fully paid parental leave, adoption and surrogacy reimbursement, and coverage for IVF and egg freezing are highlighted as standout features. Additional family-care resources and caregiving support broaden the appeal.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
