Top Tech Jobs & Startup Jobs in Boston, MA

Create developer-facing technical materials and demos for NVIDIA Omniverse libraries, SDKs, and APIs. Produce tutorials, reference architectures, blogs, videos, and launch collateral to drive developer adoption for simulation, rendering, robotics, synthetic data, and industrial digitalization workflows. Collaborate with engineering, product, marketing, and sales to identify gaps in the developer journey and translate platform capabilities into clear technical narratives and hands-on content.

The role involves optimizing GPU performance for neural reconstruction workflows, analyzing bottlenecks, and collaborating with engineers to enhance training and rendering systems.

Create and deliver developer-focused technical content and workshops that teach CUDA and NVIDIA GPU technologies. Evaluate new CUDA features, produce tutorials and blog posts, capture developer feedback to inform product decisions, and collaborate across product, engineering, and marketing teams to drive developer adoption.

Own design, automation, and lifecycle of Kubernetes platform for global network infrastructure. Build production-quality tooling for cluster provisioning, upgrades, GitOps delivery, observability, and recovery. Provide production support and incident response for network services on the platform, drive root-cause analysis, and establish platform standards and runbooks.

Develop and maintain AI networking communication frameworks, production benchmarking tools, and automation for large-scale AI training/inference. Enable models in benchmarking infrastructure, analyze end-to-end performance, perform large-scale parameter searches, and collaborate with networking and hardware teams to co-design features and optimize system performance.

Lead invention of AI-driven ASIC development methodology, identify cross-stage bottlenecks, build scalable data pipelines, define metrics, mentor engineers, and evaluate AI/EDA research to improve predictability and cycle time.

Design, build, and optimize GPU‑accelerated inference software and open‑source frameworks (vLLM, SGLang, FlashInfer). Improve performance and scale for LLMs and generative models across NVIDIA GPUs and edge SoCs using CUDA, CUTLASS, Triton, NCCL and profiling tools. Collaborate across teams to enable efficient model serving and deployment.