Top Tech Jobs & Startup Jobs

Architect and optimize large-scale GPU AI infrastructure: profile distributed training/inference, diagnose networking and system bottlenecks, design high-performance clusters, lead POCs, automate benchmarking, and drive technical engagements with customers and internal teams.

Lead strategic engagement and business development for a major CSP account: build R&D relationships, drive NVIDIA networking and accelerated computing solutions, manage forecasting, supply, pricing and contracts, coordinate product-roadmap communications, and partner with technical teams to grow revenue.

Validate clocking and reset logic for Tegra and GPU ASICs end-to-end: develop test plans, automation and validation flows, implement coverage metrics, run tests, identify/fix bugs, partner with architecture/design teams, and productize verification solutions across platforms.

Lead founding AI Safety & Security Engineering team, define harness architecture and engineering standards, drive Find/Validate/Patch roadmap, mentor engineers, and translate research into production-grade, secure ML systems.

Develop silicon-accurate GPU kernel microbenchmarks, model-level performance analysis, and agentic kernel optimization to maximize LLM inference throughput and latency. Attribute bottlenecks across kernel, compiler, and runtime, produce optimization policies, and collaborate with compiler, hardware, kernel, and framework teams to deliver production-grade performance improvements.

Develop and support high-performance infrastructure tools for chip design and verification. Build scalable backend and frontend web applications using Golang, TypeScript, and JavaScript; manage hardware-level product definitions; improve workflows for architecture, hardware, and software teams; and research solutions that increase engineering efficiency.

Lead post-silicon memory bring-up, characterization, and qualification for GDDR/LPDDR/HBM on GPU/CPU/SoC. Root-cause ambiguous multi-domain memory failures, productize fixes and playbooks, and build AI-enabled workflows to analyze shmoo/margin data and accelerate qualification across global teams.

Develop and advance multimodal agentic AI systems using deep learning, reinforcement learning, large language models, and audio-visual reasoning. Responsibilities include training and deploying foundation models, improving post-training and alignment methods, developing planning and tool-use capabilities, managing multimodal datasets and evaluation pipelines, and leading research on embodied AI, voice interaction, and long-horizon task completion.

Design, model, analyze, and validate SoC and system architectures across datacenter, automotive, and client products. Build SOC platforms, guide modeling teams, develop tests and coverage reporting, collaborate with GPU teams and EDA partners, and align cross-site modeling efforts.

Develop and maintain open-source GPU-accelerated inference software for LLMs. Enable disaggregated serving across vLLM, SGLang, and TensorRT-LLM for agentic inference (long-horizon reasoning, tool calling, multi-turn state). Optimize inference-state management, GPU memory and cache reuse, and distributed frontend performance, while prioritizing latency, throughput, and integration with upstream APIs and agent harnesses.