Top Tech Jobs & Startup Jobs

Lead NVIDIA software integration and adoption across Hitachi’s businesses, products, and engineering groups. Coordinate NVIDIA account, product, engineering, developer relations, and solution architecture teams with Hitachi stakeholders. Drive joint solutions involving AI, digital twins, edge computing, industrial automation, and accelerated computing; influence partner roadmaps, resolve technical challenges, identify opportunities, and deliver strategic partnership outcomes.

Lead developer relations and technical partner engagement for NVIDIA AI, GPU, robotics, autonomous systems, and AI agent technologies. Drive adoption through technical integrations, sample code, demos, workshops, onboarding, and solution development with strategic cloud and software partners. Collaborate with engineering, product, marketing, sales, and legal teams to resolve challenges, identify ecosystem opportunities, influence roadmaps, and scale developer programs and communities.

Build and maintain network automation and orchestration software for NVIDIA GPU Cloud and SuperPod deployments. Design/configure centralized configuration management, integrate cloud provider APIs, implement data pipelines and streaming telemetry, and automate lifecycle operations from procurement to deployment. Collaborate with infrastructure experts to deliver zero-touch, production-grade network provisioning, monitoring, and management tools.

Drive NVIDIA platform adoption across the CAE ISV and partner ecosystem. Develop ecosystem growth, promotion, and co-selling strategies; lead technical collaborations, proof-of-value engagements, business alliances, and global cross-functional programs. Partner with engineering, product, marketing, sales, OEMs, CSPs, channel partners, and customers to increase revenue, shape product direction, and communicate NVIDIA’s CAE, AI-Physics, and Agentic AI value proposition. Support customer announcements, speaking sessions, and keynote activities.

Develop innovative physical design methodologies for GPUs and SOCs, focusing on PPA improvements and collaboration across multiple teams. Use AI/ML to enhance design efficiency.

Develop platform software using container runtimes and Kubernetes to enable GPU-accelerated applications. Build GPU resource support for container orchestrators, integrate software across NVIDIA’s technology stack, and contribute to cloud-native systems. The role requires systems programming, Linux internals, distributed systems, cloud computing, and performance, security, and reliability expertise. Responsibilities include solving complex problems, managing multiple priorities, and collaborating across engineering teams.

As a GPU Simulation System Software Engineer, you'll develop infrastructure for GPU simulations, coordinate with architects, and improve workflows for next-gen GPUs.

Design, deploy, maintain, tune, and secure enterprise Linux environments supporting high-performance EDA compute farms. Lead infrastructure-as-code automation, configuration management, continuous deployment, and tooling development using Python, Bash, or Go. Manage LDAP/SSSD naming services, NFS and Autofs storage, virtualization, container infrastructure, registries, networking, and infrastructure telemetry. Support highly available, scalable systems and potentially integrate cloud platforms, CI/CD pipelines, EDA toolchains, and HPC clusters.

Manage software new product introduction programs for GPU-based products, coordinating Software, Hardware, Marketing, Operations, and QA teams. Responsibilities include negotiating system and chip requirements, driving hardware allocation and budgets, representing Software in go-to-market meetings, maintaining project plans, resolving impediments, analyzing program performance, and improving development processes. The role requires software lifecycle knowledge, device driver and application experience, chip bring-up expertise, technical communication, and multitasking skills.

Develop Rust-to-GPU compiler pipelines, custom intermediate representations, optimizers, and ahead-of-time, just-in-time, and link-time compilation tooling for NVIDIA GPUs. Design Rust-based safety abstractions across GPU execution boundaries and expose low-level CUDA hardware features. Responsibilities include compiler architecture, GPU code generation, debugging, profiling, benchmarking, and independently driving complex engineering initiatives from research through production.