Top Tech Jobs & Startup Jobs in Chicago, IL

Lead technical partnership with Alphabet teams to drive adoption and integration of NVIDIA libraries, models, and frameworks. Coordinate cross-functional engineering and GTM teams, resolve integration challenges, influence product roadmaps, and deliver joint solutions that accelerate partner workloads and developer adoption.

Research and develop GPU-acceleration techniques for high-performance databases, ETL, and data analytics applications. Analyze data-intensive workloads, optimize algorithms, memory management, and CUDA kernels, and investigate CPU/GPU bottlenecks. Collaborate with research, hardware, software, and industry teams to influence next-generation architectures, programming models, and data-processing solutions.

Design, develop, test, and extend NVIDIA’s large-scale object storage platform. Responsibilities include improving availability, reliability, durability, fault tolerance, and performance across deployments exceeding 10,000 nodes and exabytes of data. The role also automates storage infrastructure provisioning, management, and monitoring; supports AI/ML research teams; owns product delivery through support; and contributes to open-source storage projects.

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.

Partners with customers and internal teams to design, deploy, and optimize generative AI and LLM inference solutions. Develops proofs of concept, supports NVIDIA technology adoption, analyzes AI workload performance and power efficiency on Kubernetes, and advises developers, researchers, and data scientists. The role requires expertise in deep learning frameworks, GPU orchestration, MLOps, containerization, observability, and LLM inference, with occasional travel to conferences and customers.

Develop HPC and AI software workflows for experimental data processing, imaging, plasma, high-energy physics, and materials simulations. Profile applications, benchmark performance, identify bottlenecks, and advise collaborators and product teams. Build tutorial examples, translate research into software tools, and represent NVIDIA at scientific conferences and industry events.

Lead and grow an engineering team building GPU‑accelerated deep learning inference frameworks. Drive roadmap, collaborate with compiler/libraries/research teams, optimize LLM and multimodal model performance, and oversee multi‑GPU communications and production deployment of inference software.

As a Senior Solutions Architect, you will assist customers in building solutions with NVIDIA's AI technology, focusing on Generative AI and Large Language Models while collaborating across teams for performance analysis.

Senior software engineer on Developer Tools working across frontend and backend to design, implement, and ship profiling features for cloud and cluster environments. Collaborate with cross-functional teams, interact with users, and deliver end-to-end product features from prototypes to demos focused on collecting, analyzing, and visualizing performance data.

Design and implement high-performance communication protocols and algorithms for NVIDIA networked supercomputers and datacenters. Develop production-grade communication frameworks and applications, optimize performance and scalability, and collaborate with networking chip design teams on hardware/software co-design.