Top Tech Jobs & Startup Jobs in Boston, MA

Support NVIDIA enterprise customers using GPU-accelerated platforms, triage hardware and AI/ML workload issues, collaborate with engineering teams, and drive cases through resolution. Develop support tools and products using AI, document customer interactions, and troubleshoot Linux-based multi-GPU systems, firmware, kernels, drivers, containers, and distributed workloads. The role also requires customer communication, technical analysis, and occasional weekend or holiday support.

Verify ASIC inference accelerator designs at block and SoC levels using advanced methodologies, random stimulus, functional coverage, assertions, and UVM. Develop verification infrastructure, debug designs, optimize automated flows, and collaborate with architects, designers, and pre- and post-silicon teams. Apply knowledge of ASIC design flow, power verification, DFT, netlist verification, and machine learning techniques to improve verification processes.

Own and evolve Kubernetes networking for GPU superclusters spanning multiple clouds and thousands of nodes. Design and operate CNI data planes, overlay meshes, VPNs, gateways, load balancers, and tunnels. Identify scale limitations, build network validation environments, diagnose distributed networking failures, and partner with cloud providers on topology. Provide technical leadership while using strong Linux networking, systems programming, and large-scale debugging expertise.

Develop low-level software for NVIDIA NVLink Fusion, including kernel-mode drivers, embedded firmware, and microcode. Lead features through architecture, pre-silicon verification, silicon bring-up, validation, production, and partner deployment. Build tests and diagnostics, debug hardware-software issues, and collaborate with architects, ASIC designers, firmware teams, validation engineers, and external partners. Use C, C++, and scripting languages while contributing to design reviews, release management, and software quality.

Develop and support NVIDIA networking technologies for AI clusters, including InfiniBand, NVLink, and Spectrum-X. Reproduce and root-cause complex customer issues down to source code, collaborate with R&D on fixes, analyze high-performance network metrics, and create diagnostic tools. The role requires extensive C and Python development in Linux and embedded systems, networking expertise, customer support experience, and strong communication skills. Occasional weekend or holiday support may be required.

Provides advanced software and customer support for NVIDIA networking technologies used in AI clusters. Reproduces and root-causes complex field issues to the source-code level, develops fixes and workarounds with R&D, analyzes high-performance network metrics, builds diagnostic tools, and supports technologies including InfiniBand, NVLink, and Spectrum-X. The role requires extensive C or Python experience in Linux or embedded systems, networking expertise, and strong customer communication.

Provide advanced technical support for NVIDIA Ethernet switching and AI infrastructure products. Troubleshoot Linux, server, hardware, operating system, networking, installation, interoperability, and operational issues for enterprise customers. Analyze protocols and packet captures, lead critical issue resolution, collaborate with Engineering, Marketing, and Support, and develop support methodologies and documentation. The role focuses on data center, HPC, networking, and AI infrastructure environments.

Provides advanced technical support for NVIDIA InfiniBand, NVLink, GPU, networking, and HPC infrastructure products. Responsibilities include troubleshooting customer issues across installation, operations, maintenance, interoperability, Linux environments, and performance; responding through multiple channels; collaborating with engineering and marketing; and documenting support methodologies. The role requires deep networking, Linux administration, virtualization, cloud, containerization, and HPC expertise, along with strong communication and customer problem-solving skills.

Develop GPU-accelerated techniques for generative AI training and inference, optimize algorithms and performance across NVIDIA CPU and GPU architectures, contribute to frameworks, libraries, and open-source projects, and collaborate with developers, researchers, and hardware and software engineering teams. The role focuses on LLMs, diffusion models, multimodal systems, low-level optimization, and communicating innovations through technical publications and industry conferences.

Leads research advancing AI models and agents for cybersecurity, including reasoning, tool use, vulnerability discovery, threat detection, malware analysis, incident investigation, and automated defense. Defines research agendas, develops and evaluates prototypes in realistic security environments, improves robustness and explainability, collaborates across research and engineering teams, publishes findings, mentors researchers, and translates foundational advances into deployed security capabilities.