Top Tech Jobs & Startup Jobs in Los Angeles, CA

Develop and improve NVIDIA’s proprietary GPU compiler backend in C++. Design register allocation passes and compiler optimizations for high-performance GPU machine instructions. Collaborate with compiler, hardware, and application teams on improvements and issue resolution. The role spans deep-learning compiler technology and requires strong compiler code-generation experience, preferably with LLVM, plus robust software engineering, testing, and documentation skills.

Lead strategic developer engagements to drive adoption of NVIDIA AI and accelerated computing. Architect integrations, deliver reference implementations and demos, mentor DevRel teams, and collaborate with partners and internal stakeholders to scale production-ready MarTech and AI solutions using NVIDIA software and hardware.

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.

Define high-level SoC subsystem and I/O architecture for LPU products, translate product requirements into architecture and IP specifications, build and validate functional/architectural models (C++, SystemC, Python), and drive tradeoffs across performance, power, reliability, and security while coordinating with IP, firmware, software, RTL, verification, and post-silicon teams.

Design, develop, test, and optimize system software for next-generation ARM SoCs in pre- and post-silicon phases. Analyze system-wide performance bottlenecks, recommend HW/SW policies, perform competitive workload comparisons, and drive full-stack adoption of performance optimizations across NVIDIA software and OSS SDKs.

Lead developer advocacy for payments, driving adoption of NVIDIA AI and computing platforms. Support partner integrations, provide code samples and demos, research ecosystem trends, collaborate with engineering and partners, present at conferences, and promote co-innovation for payments use cases like fraud detection and personalization.

Serve as technical lead between NVIDIA and hyperscale CSPs on GPU firmware and system software. Drive firmware update orchestration, recovery procedures, multi-GPU coordination, telemetry/health monitoring, and incorporate CSP feedback into NVIDIAs roadmap to improve fleet-scale manageability and reliability.

Lead developer relations for CAE by building technical expertise, integrating NVIDIA software stacks into partner projects, supporting onboarding with code samples and demos, monitoring ecosystem trends, and collaborating with partners and internal teams to drive adoption of NVIDIA AI and accelerated computing technologies.

Lead technical engagements with cloud service providers on rack-scale system software and firmware. Drive architecture alignment, integration readiness, error handling and recovery, health telemetry, firmware orchestration, and serviceability. Capture CSP feedback, influence NVIDIA system software design, improve tooling and tests, and mitigate execution risks through early collaboration.

Serve as NVIDIA's CSP-facing principal for end-to-end performance: drive characterization with hyperscaler engineering teams, gather and synthesize workload feedback, prioritize optimizations across CUDA/NCCL/driver/firmware, validate benchmarks and tools, update stress/profiling tooling, define test strategies, and ensure performance integration and acceptance for customer-representative configurations.