Top Tech Jobs & Startup Jobs in Los Angeles, CA

Develop and deploy deep learning models and petabyte-scale multimodal data curation pipelines for foundation-model training. Responsibilities include document extraction, OCR, layout and table analysis, deduplication, distributed processing, dataset and metric development, experimentation, production scaling, and deployment through NVIDIA Inference Microservices. The role also involves publishing research, creating technical documentation, communicating findings, and mentoring team members.

Lead architecture for cloud networking and security solutions involving DPUs and NICs. Design end-to-end systems from application to hardware, define architecture specifications, evaluate technologies, develop proof-of-concept prototypes, and collaborate with global software, hardware, product, engineering, and customer teams. The role requires strong C and Python development, Linux, networking protocols, cloud technologies, and systems architecture expertise.

Design, develop, optimize, and debug ultra low-latency C++ streaming systems and media pipelines (WebRTC, GStreamer, RTP). Analyze performance across CPU, memory, networking, and scheduling. Integrate streaming software with platform services across Windows, Linux, QNX, and embedded platforms. Define KPIs for networking and streaming quality and troubleshoot across software, OS, platform, and hardware boundaries.

Design and develop RTOS and hypervisor features, bring up and optimize software stacks on NVIDIA Tegra SoCs, and debug system-level issues across hardware, firmware, RTOS, and virtualization layers. The role develops high-integrity automotive software using safety and security practices, while leading cross-team technical initiatives and coordinating execution. Candidates need strong C/C++ systems programming, embedded software expertise, operating-system knowledge, hardware-assisted virtualization experience, and at least eight years of experience.

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 NVIDIA’s RL post-training frameworks strategy and engineering ecosystem across distributed training, inference, rollout, evaluation, orchestration, and NVIDIA platforms. Build and manage globally distributed teams, prioritize upstream and internal investments, establish benchmarks and execution metrics, and drive reliable open-source integrations. Partner with research, product, hardware, CUDA, networking, and external communities to scale reinforcement learning workloads across GPUs and heterogeneous systems.

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 the technical narrative and content strategy for NVIDIA DSX, an AI factory platform spanning facilities, power and cooling, compute, networking, storage, software, orchestration, simulation, models, and workloads. Develop reference architectures, whitepapers, guides, demos, workshops, training, videos, and launch assets. Coordinate cross-functional teams and partners, lead flagship demos, identify ecosystem opportunities, influence roadmap and positioning, mentor contributors, and measure content adoption.

Drive the development and roadmap of HPC and AI/ML products for physics, plasma, high-energy-density simulations, and resilient materials discovery. Define features, requirements, positioning, and go-to-market strategy; collaborate with engineering and NVIDIA software teams; identify customers and partnerships; and represent products at workshops and conferences.

Lead the architecture, prototyping, deployment, and scaling of agentic AI platforms for NVIDIA Marketing. Translate marketing opportunities into reliable AI agents, reusable platform capabilities, and production architectures. Establish practices for evaluation, observability, security, governance, reliability, and cost optimization while partnering with engineers on integrations, tooling, retrieval, memory, orchestration, and infrastructure. Guide complex initiatives from discovery through ongoing operations and communicate technical solutions to technical and business stakeholders.