Top Tech Jobs & Startup Jobs in NYC, NY

The role involves designing and developing VLSI CAD software, collaborating in agile practices, and advancing design tools for chip development.

The role involves partnering with various teams to drive NVIDIA's product features, optimize ML models, and ensure customer satisfaction in financial services.

This role involves researching and optimizing applications for NVIDIA's computing platform, collaborating with customers and internal teams to enhance performance and design next-generation software and hardware.

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.

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.

Lead technical engagements with cloud partners to design, integrate, and deploy large-scale GPU AI/HPC infrastructure. Drive proof-of-concepts, production delivery, performance tuning, and adoption of deep learning frameworks, models, and NVIDIA software across the customer lifecycle, while collaborating cross-functionally and presenting technical content.

Partner with engineering, product, and sales to design and deliver GPU-accelerated ML/HPC solutions for capital markets. Lead POCs, profile and optimize ML/DL models on NVIDIA GPUs, collaborate with client researchers to deploy models at scale on cloud or on-prem HPC, and produce technical collateral (notebooks, blogs) to drive adoption.

Lead GPU and HPC cluster architecture and deployment for cloud partners: design end-to-end GPU/NVLink infrastructures, perform performance modeling, validate and debug cluster configurations, support NPI deployments, and serve as technical liaison between engineering, product, and field teams.

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.