Top Tech Jobs & Startup Jobs in Boston, MA

Verify ASIC/GPU and SoC designs using advanced verification methodologies. Define verification scope, develop test plans and test generators, build verification infrastructure, and collaborate with architects, designers, and pre/post-silicon teams to validate CPU/GPU interactions and memory coherence.

Design and implement end-to-end GPU performance verification infrastructure. Build scalable data pipelines to collect metrics from simulation, emulation, and silicon. Collaborate with GPU architects and hardware teams to automate verification workflows, provide performance insights and modeling, and accelerate GPU build iteration.

Design and implement reliable, scalable full‑stack web tools (backend and frontend) to automate and optimize chip design and verification workflows. Build web applications in Go, TypeScript, and JavaScript, model hardware-level product definitions, research solutions to improve engineering efficiency, and support hundreds of global users.

Develop system software components, collaborate on SOC designs, support DPU projects, optimize system-level resource utilization, and engage in all product development lifecycle stages.

As a System Software Engineer at NVIDIA, you'll develop GPU architecture, improve performance testing, and enhance workflows for chip designers, working closely with both software drivers and GPU simulations.

The role involves developing system software for NVIDIA's DPU, including firmware and drivers, collaborating with hardware teams, and tackling system-level optimizations.

Develop and support software tools for chip design and verification, optimize workflows, work collaboratively to create high-performance software.

Lead development and validation of LLM inference platforms for NVIDIA edge GPUs. Track open-source framework innovations, analyze model-to-hardware mapping, optimize GPU kernels and multi-node inference, produce performance reports, maintain developer inference recipes, own model bring-up workflows, and support partners on hardware-specific inference issues.

Lead design and implementation of scalable communication technologies for AI and HPC. Identify performance bottlenecks, co-design HW/SW, build proofs-of-concept, run experiments and simulations at large GPU-cluster scale to accelerate deep learning and HPC workloads.

The role involves researching and developing GPU-accelerated AI workloads, optimizing algorithms, and collaborating with technical experts to improve hardware performance.