Top Tech Jobs & Startup Jobs

Develop and maintain automated tests and OS verification infrastructure for SDKs, improve CI/CD and automation workflows, collaborate with hardware/firmware/DevOps to reproduce and debug issues, participate in reviews, and grow ownership of verification areas.

Define next-generation data center GPU products by analyzing workloads, especially large language model training and inference. Identify customer opportunities, develop GPU value propositions and product proposals, and collaborate with engineering, operations, field teams, cloud partners, and sales on go-to-market plans, positioning, collateral, and tools. The role requires technical knowledge of GPU, software, and computing architectures, along with 7+ years of technology experience.

Lead cross-functional programs for NVIDIA DGX Cloud, bridging cloud providers and engineering. Collect requirements, prioritize, plan roadmaps, manage schedules, risks, and quality. Drive communication, process improvements, and organizational efficiencies while coordinating global teams and ensuring delivery using Agile practices and tools like Jira/JAMA.

Design, implement, and improve NVIDIA’s CUDA Driver and programming models, including CUDA Graphs and GPU kernel scheduling for AI/ML workloads. Lead cross-team development, define CUDA API improvements, develop well-tested code across operating systems, and address system-level challenges involving operating systems, memory, threads, and GPU architecture.

Lead energy-focused product marketing and go-to-market strategies for NVIDIA AI, accelerated computing, simulation, and digital twin solutions. Develop messaging, content, sales enablement, analyst and PR engagement, market segmentation, competitive intelligence, and performance analytics. Collaborate with marketing, sales, product, engineering, research, partners, and executive stakeholders to drive AI adoption across utilities, power, oil and gas, and industrial energy markets. Represent NVIDIA at conferences, executive forums, and partner events.

Lead architecture and development of Vehicle and Sensor Abstraction Layers (VAL/SAL) for autonomous vehicles. Manage teams building low-latency C++ systems with GPGPU (CUDA) acceleration, interfacing sensors and actuators, ensuring scalable integration, safety-compliant automotive software (MISRA), and validating via real-world and synthetic testing. Oversee in-vehicle tests, data analysis, unit testing, documentation, and cross-functional collaboration to deliver production-quality autonomy software.

Architect and develop scalable SaaS, web APIs, SDKs, CLIs, and AI-assisted developer tools for coding, debugging, profiling, and CUDA optimization. Collaborate with Product, Design, and engineering teams to define features, improve cloud infrastructure, and deliver reliable services for concurrent developers. Mentor engineers and review code and designs while applying Kubernetes, AWS, infrastructure as code, DevOps, Python, virtualization, and GPU profiling expertise.

Design, build, deploy, and operate cloud infrastructure and scalable I/O systems for scientific computing, physics simulations, AI workflows, and data-intensive workloads. Optimize compute, storage, and network architectures across CPU and GPU environments. Develop algorithms, debug systems and third-party code, and improve performance across distributed systems, operating systems, kernels, compilers, and parallel-processing frameworks while collaborating independently across teams.

The Senior GPU Memory Architect will develop memory architecture, perform simulations, analyze performance, define experiments, and create specifications for GPU systems.

Lead and develop the MD Simulation Engineering team, focusing on GPU-accelerated simulation software and managing cross-functional partnerships to drive project success.