2 days ago
Base Salary
$152k - $288k/yr
Responsibilities
- Design and implement end-to-end Grove integrations with open-source AI frameworks including Dynamo, llm-d, Ray, and PyTorch.
- Build and maintain adapters, plugins, operators, and runtime components for training and inference stacks.
- Collaborate with framework owners and open-source communities to upstream changes and maintain integrations.
- Develop reference workflows, sample applications, best-practice guides, and developer-focused tooling.
- Optimize performance, scalability, and reliability for distributed multi-node and multi-GPU workloads.
- Improve observability and operational readiness through metrics, logging, tracing, debugging tools, and Kubernetes-based deployment support.
- Define APIs and contracts, ensure compatibility across framework and dependency versions, and diagnose complex infrastructure and runtime issues.
Requirements
- Bachelor's, master's, or doctoral degree 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 a major AI framework or runtime such as PyTorch, Ray, or the Triton Inference Server ecosystem.
- Understanding of AI workloads, model development fundamentals, training and inference tradeoffs, and throughput, latency, batching, and memory considerations.
- Experience with distributed systems concepts including scheduling, fault tolerance, and resource management.
- Practical Kubernetes experience deploying and operating services and jobs, with familiarity with Helm, Kustomize, and operators/controllers.
- Familiarity with containers, container registries, and cloud-native tooling.
- Strong software engineering experience in Go, C++, and/or Python with a record of shipping reliable systems.
- Strong collaboration, communication, and documentation skills.
- Preferred experience includes open-source contributions, large-scale model serving, distributed inference, multi-tenant AI platforms, SDKs/APIs, developer tooling, GPU profiling, kernel-level optimization, reproducibility, packaging, versioning, and compatibility testing.
Benefits
- Eligible for equity and benefits.
- Base salary varies by location, experience, and comparable employee pay.
- Applications accepted at least until September 24, 2026.
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.