Top Tech Jobs & Startup Jobs in NYC, NY

As a Senior AI Developer Technology Engineer at NVIDIA, you'll design and optimize AI and HPC workloads, solve performance issues, and influence future hardware and software design.

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

Leads technical developer-relations strategy for NVIDIA platforms across capital markets. Advises quants, traders, developers, financial institutions, ISVs, and cloud providers; demonstrates CUDA, CUDA-X, AI frameworks, and accelerated computing solutions; supports partner onboarding and integrations; develops benchmarks, sample code, and reference implementations; gathers ecosystem feedback; influences product roadmaps; and represents NVIDIA at industry events, workshops, and conferences.

Provide technical support to NVIDIA Enterprise customers by triaging and resolving issues, working with engineering on reproductions/logs, applying AI/ML and agentic AI to build support tools and products, documenting interactions and knowledge, and owning cases to resolution (occasional weekend/holiday support).

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.

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.

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.

Design, build, deploy, and operate large-scale AI training and inference infrastructure. Responsibilities include distributed systems performance analysis, infrastructure automation, capacity management, service lifecycle ownership, monitoring, reliability improvement, incident response, and on-call support for multi-GPU and multi-node cloud platforms.

Translate cutting-edge agent research into secure, production-grade runtime systems: prototype, benchmark, reproduce methods, build agent harnesses and evaluation/red-team workflows, secure agent toolchains, and partner across research, product, and engineering to integrate features into OpenShell.