Senior Software Engineer, AI Frameworks

🕒 March 31

🏄 California – Remote

🏄 California – Remote

💵 $152k - $287.5k / year

⏰ Full Time

🟠 Senior

🤖 AI Engineer

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 56%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• Drive integration of the NVIDIA Grove project within Dynamo and across leading open-source AI frameworks • Develop production-grade software enabling Grove capabilities to be adopted, scaled, and operated across environments such as Dynamo, llm-d, Ray, PyTorch, and other AI frameworks • Design and implement end-to-end Grove integrations with open-source AI frameworks • Build and maintain adapters, plugins, operators, and/or runtime components for training and inference stacks • Partner with framework owners to upstream changes, contribute patches, and maintain integrations • Develop reference workflows, sample applications, and best-practice guides • Optimize performance, scalability, and reliability for distributed training/inference in multi-node and multi-GPU environments • Improve observability and operational readiness for Kubernetes-based deployments through metrics, logging, tracing, and debugging tools • Participate in technical design reviews, define APIs/contracts, and ensure compatibility across framework and dependency versions • Diagnose issues involving containers, networking, scheduling, CUDA/GPU utilization, and framework runtime behavior • Collaborate across engineering teams and the open-source community to deliver integrations, reference implementations, and developer tooling

🎯 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 (standout) • Experience with large-scale model serving, distributed inference, or multi-tenant AI platforms (standout) • Experience building SDKs/APIs or developer tooling that improves integration usability (standout) • Knowledge of GPU performance profiling and optimization (Nsight tools or similar), and/or kernel-level performance tuning (standout) • Experience with reproducibility, packaging, versioning, and compatibility testing across fast-moving dependencies (standout)

🏖️ Benefits

• Equity • Benefits

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume