Top Tech Jobs & Startup Jobs in Los Angeles, CA

Build, scale, and harden deep learning training infrastructure for multi-thousand GPU clusters. Improve data loaders, distributed training, scheduling, and performance monitoring. Develop fault-resilient orchestration, training pipelines for massive video datasets, and collaborate with researchers and platform teams to maximize training efficiency, availability, and scalability.

Lead development and improvement of ASIC integration, build, simulation, CI, and debug infrastructure for high-speed IO GPU IP. Apply automation, metrics, and AI-assisted workflows to improve reliability, turnaround, and debuggability. Collaborate across design, verification, firmware, methodology, and infrastructure teams to root-cause failures and deliver durable process improvements.

Lead developer relations with US national labs and federal research: build strategic collaborations, embed NVIDIA quantum SDKs, run workshops and trainings, present at conferences, create reference implementations, and relay research needs to product and engineering teams to drive adoption of CUDA-Q, cuQuantum, CUDA-Q QEC, and NVQLink.

Design and perform physical layout for silicon interposers using Cadence Virtuoso; run DRC/LVS and verification (Calibre, ICV, Thunder); perform feasibility studies, floorplanning, custom layout, and collaborate with package, SI, and technology teams to meet schedule, cost, manufacturability, and electrical goals.

Research and implement GPU-acceleration techniques for high-performance databases and ETL. Analyze and optimize data-intensive workloads, collaborate with hardware, systems, and research teams to influence next-generation architectures and programming models, and publish/share findings with customers and the community.

Lead architecture for cloud-networking and design system architectures for DPUs and NICs. Build end-to-end solutions from application to hardware, write architecture specifications, evaluate new technologies, develop POCs, and collaborate globally with SW/HW teams and customers.

Lead architecture and global strategy for large-scale, low-latency distributed AI inference. Design high-throughput model serving, hardware-software co-optimization, orchestration for deployment/versioning/auto-scaling across cloud and datacenters, collaborate with customers and open-source ecosystems, and drive full software and system lifecycle for production reliability and performance.

Deploys and validates complete AI infrastructure software stacks on multi-node GPU systems, then converts implementations into technical documentation, automation, demos, training, and reference architectures. Tests prerelease software, evaluates interoperability and operational resilience, supports partners and field teams, collaborates with engineering and open-source communities, and recommends product improvements based on customer feedback. The role presents solutions through briefings, workshops, webinars, events, and internal training, with some travel required.

Lead strategic sales and business development for NVIDIA Networking at a major Silicon Valley account. Build relationships with R&D and technical teams, drive adoption of GPU-accelerated and AI solutions, manage forecasting, supply and pricing, liaise between customers and product teams, and collaborate with engineering and solutions architects to qualify and deploy networking platforms. Travel up to 25%.

Prototype and integrate GPU-accelerated distributed data processing, compression, databases, and analytics solutions. Optimize complex data-intensive workloads across heterogeneous CPU/GPU architectures, collaborate on next-generation hardware and software designs, and work with customers and cloud service providers to deploy solutions and influence open standards. The role requires deep expertise in parallel programming, C/C++, accelerator architecture, memory systems, and compression or distributed data systems.