Top Tech Jobs & Startup Jobs in Los Angeles, CA

Translate cutting-edge agent research into secure, production-grade runtime systems: prototype, benchmark, reproduce methods, build agent harnesses and evaluation/red-team workflows, secure agent toolchains, and partner across research, product, and engineering to integrate features into OpenShell.

Lead and deliver compiler software programs across cross-functional teams using Agile and Waterfall. Drive process development, coordinate global partners, report status to executives, manage risks, and resolve technical/resource conflicts to ship compiler and GPU-related releases.

The role involves integrating AI supercomputing systems into datacenters, collaborating with engineering teams, and producing documentation for processes related to datacenter integration and fit-out.

Develop and maintain CAE solver benchmarks on NVIDIA platforms, create technical marketing content (benchmarks, guides, briefs), present findings, and produce training/documentation to enable developers to adopt GPU-accelerated CAE workflows.

Lead technical program management initiatives for pre-silicon software enablement at NVIDIA, focusing on hardware-software integration and dependency resolution.

As a Principal System Architect, you'll define GPU architecture, lead cross-functional teams, evaluate power and performance, and mentor junior engineers in SoC development.

The role involves developing AI-driven compiler technologies, optimizing workflows, integrating learning systems, and collaborating on next-gen compiler capabilities for improved performance.

As a Senior Solutions Architect, you will lead GPU-accelerated AI solutions development in healthcare, working alongside pharma companies to optimize simulations and robotic systems. You'll implement projects from concept to deployment while collaborating with various stakeholders.

Design, develop, and operate massively distributed GPU cluster platforms for DGX Cloud. Build tooling to identify, diagnose, and remediate non‑performant GPU assets; ensure reliable, high‑performance production AI clusters; collaborate across teams; evaluate failures and improve services; work across frontend and backend stack.

Architect and optimize large-scale GPU AI infrastructure: profile distributed training/inference, diagnose networking and system bottlenecks, design high-performance clusters, lead POCs, automate benchmarking, and drive technical engagements with customers and internal teams.