Top Tech Jobs & Startup Jobs in Los Angeles, CA

Lead hardware-software co-design for LLM inference: drive and document architecture, build executable C++ hardware models, optimize performance with good algorithms and parallelism, diagnose cross-team chip and subsystem issues, and automate workflows to improve efficiency.

Drive developer enablement for CUDA-Q by creating docs, examples, tooling, MCP servers, Agent Skills, prompt templates, and benchmarks. Analyze developer and agent workflows, define documentation and agent-consumable standards, run evaluations and metrics, and translate research into practical guidance to improve developer time-to-value for quantum and GPU-accelerated applications.

Lead GPU and NVLink-based cluster design and validation for large-scale AI and HPC deployments. Advise cloud partners on architectures, perform performance modeling, debug deployment issues, support NPI rollouts, and relay field feedback to engineering.

Design and implement functional GPU architecture models and simulation platforms. Develop tools, tests, and validation infrastructure, collaborate with architecture and engineering teams, and apply AI/ML to improve simulation and analysis workflows.

Own and optimize IBM Spectrum LSF scheduling across 15–25 federated cells. Diagnose scheduler latency, MultiCluster forwarding and cross-cluster contention, design scalable cell topology, and encode scheduler policy through infrastructure as code. Partner with CAD and methodology teams on large-scale EDA workloads, license constraints, and tape-out bursts. Requires deep LSF internals expertise, production MultiCluster experience, strong Linux fundamentals, and Python, Perl, and shell scripting skills.

Design and implement CUDA Tile compiler transformations, MLIR dialects, lowering passes, and optimization techniques for NVIDIA GPUs. Optimize tile-based kernel performance across GPU generations, define public APIs, conduct performance analysis, and develop robust software, tests, and debugging workflows. The role requires independent project ownership and collaboration within a product-focused engineering team.

Designs, deploys, maintains, and tunes large-scale HPC and AI clusters. Responsibilities include workload scheduling, infrastructure automation, CI/CD pipelines, monitoring, logging, alerting, storage and networking deployment, bottom-up troubleshooting, methodology documentation, and R&D support. The engineer works across hardware, operating systems, GPU computing, virtualization, cloud platforms, and software stacks to deliver performant, reliable self-service infrastructure.

The role involves validating clocking structures in products, developing test plans, automation, and partnering with design teams to ensure high-quality verification of ASIC designs.

Lead a small team building a new Linux kernel-mode GPU driver: design architecture, implement device/kernel-mode code, collaborate with and upstream patches to the Linux kernel community, and represent NVIDIA in open-source discussions.

Develop end-to-end perception solutions for autonomous driving, leveraging deep learning and data-driven approaches to enhance system accuracy and efficiency.