Top Tech Jobs & Startup Jobs in Boston, MA

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.

The Senior Formal Verification Engineer will verify sophisticated ASIC builds using formal methods, collaborate with architecture teams, develop verification environments, and support silicon debug.

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.

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.

Lead and scale an engineering team building GPU-accelerated deep learning inference frameworks. Drive strategy and roadmap, partner with compiler/libraries/research teams, oversee performance tuning and multi-GPU optimizations, mentor engineers, and deliver production-ready inference pipelines for LLM, multimodal, and generative AI across datacenter and edge platforms.

Lead product definition, execution, and go-to-market for AI physics models, libraries, and frameworks. Collaborate with engineering, developer relations, marketing, and sales; present to internal and external audiences; prioritize projects; translate technical capabilities into developer and customer value.

Develop and optimize GPU-accelerated algorithms for quantum circuit simulations, collaborating with various teams, and leading technical efforts.

Design, implement, and optimize scalable high-performance dense linear algebra kernels for GPUs. Provide technical leadership, collaborate with product and partners, improve performance and quality, and contribute to library roadmaps and development practices.

Design and implement an LLVM-based JIT backend for NVIDIA GPUs, develop compiler passes and optimizations to meet performance and compile-time targets, collaborate with hardware and application teams to resolve issues, apply modern compiler technologies, and influence future GPU architectures.

Lead technology strategy and system architecture for next-generation wireless infrastructure. Apply AI/ML to RAN (L1/L2+) and transform core to agentic AI-based packet core. Prototype GPU-accelerated algorithms, engage partners and standards bodies, and drive performance optimizations and new edge applications (ISAC, SemCom, Physical AI).