Top Tech Jobs & Startup Jobs in Los Angeles, CA

Research and develop AI-native numerical algorithms that combine machine learning with scientific and engineering solvers. Build solver-in-the-loop pipelines, learned preconditioners, AI-guided multigrid methods, differentiable solver components, and hybrid ML/numerical methods. Evaluate convergence, stability, accuracy, robustness, failures, resource use, and performance. Collaborate across NVIDIA Research, GPU computing, simulation, engineering, semiconductor, and digital twin teams while shaping the applied research agenda.

Leads NVIDIA’s global partner marketing strategy with cloud service providers, driving adoption of accelerated computing platforms. Oversees joint marketing plans, integrated campaigns, events, executive relationships, Market Development Fund budgets, performance reporting, and cross-functional collaboration across product, sales, communications, and partner teams. The role requires extensive high-tech marketing experience, global program leadership, team management, financial analysis, and executive communication.

Design and implement compiler transformations, distributed runtimes, pipeline parallelism, sharding, and communication-computation overlap for large-scale deep learning training on GPU clusters. Develop JAX and XLA integrations, optimize compiler and runtime performance, support open-source JaxPP, collaborate with framework and hardware teams, and debug large training workloads.

Lead technical developer relations and partnership programs for NVIDIA’s global systems integrators and technology partners. Drive adoption of NVIDIA AI and computing platforms through technical enablement, demonstrations, integrations, workshops, and ecosystem development. Advise partner engineering teams, address integration challenges, influence product roadmaps, and collaborate with engineering, product, marketing, sales, and legal teams. Focus areas include generative AI, physical AI, agentic systems, GPU acceleration, cloud computing, and vertical-specific AI solutions.

Lead NVIDIA’s AI for Quantum product strategy, roadmap, and go-to-market initiatives. Translate AI and quantum research into scalable platforms, developer tools, APIs, libraries, and workflows. Partner with researchers, engineers, quantum hardware companies, institutions, and developers to identify applications across quantum design, control, calibration, error correction, and algorithms. Evangelize the platform through technical communications, identify new AI, HPC, and quantum opportunities, and present product strategy to executives.

Designs and builds intelligent compiler systems, compiler-oriented agents, code transformation workflows, and numerical verification infrastructure. The role focuses on low-level IR, code generation, optimization, GPU performance, differential testing, correctness validation, and integration across compiler, runtime, library, hardware, and CUDA teams. Candidates should have strong compiler or verification experience, C/C++ and Python skills, GPU programming expertise, and familiarity with frameworks such as LLVM, MLIR, PTX, or Triton.

Develop algorithms and high-performance systems across the GenAI lifecycle, including multimodal data preparation, distributed foundation-model training, reinforcement-learning post-training, inference optimization, and open-source AI infrastructure. The role involves improving scalability, efficiency, deployment cost, and model support while collaborating with research, product, and infrastructure teams.

Lead the design and implementation of a production GPU-accelerated ray-tracing engine for real-time radio propagation, wireless network simulation, radio maps, and per-link channel updates. Develop GPU algorithms, spatial data structures, multi-GPU scaling strategies, and adaptive temporal-coherence techniques for 5G and 6G RAN applications. Apply electromagnetic propagation knowledge to validate reflection, transmission, diffraction, and scattering models.

Create and deliver developer-focused technical content and workshops that teach CUDA and NVIDIA GPU technologies. Evaluate new CUDA features, produce tutorials and blog posts, capture developer feedback to inform product decisions, and collaborate across product, engineering, and marketing teams to drive developer adoption.

Leads technical engagements with Tier-1 telecommunications customers and partners, mapping workloads to NVIDIA technologies. Designs scalable architectures, integration milestones, and deployment plans; validates solutions; troubleshoots complex issues; enables platform adoption; and communicates customer needs to engineering and product teams. The role requires deep telecom stack expertise, customer-facing technical leadership, and experience with software, hardware, AI inference, networking, containers, Kubernetes, and potentially CUDA/GPU programming.