Top Tech Jobs & Startup Jobs in Los Angeles, CA

Develop and optimize resilient, large-scale cloud services supporting AI training workloads. Design Kubernetes-orchestrated microservices, APIs, job submission abstractions, lifecycle automation, checkpointing, telemetry, and failure recovery systems. Collaborate on distributed systems spanning cloud and on-premises AI clusters, integrating with NVIDIA resiliency infrastructure and HPC platforms. The role requires backend development experience, cloud and container expertise, and familiarity with AI frameworks, schedulers, GPUs, and high-performance computing.

Leads NVIDIA’s internal S3-compatible distributed object storage platform team, owning roadmap execution, software development, production operations, reliability, scalability, and service delivery at exabyte scale. Responsibilities include managing and developing engineering talent, driving SLOs, capacity planning, incident response, CI/CD, observability, customer collaboration, and AI-assisted development adoption. The role requires deep distributed storage expertise and hands-on experience operating cloud storage services for large-scale AI infrastructure.

Build, validate, deploy, tune, monitor, and retire high-confidence security detections across SIEM, endpoint, cloud, identity, SaaS, developer, and AI environments. Translate incidents, threat intelligence, hunts, and red-team findings into tested detection logic. Improve detection-as-code workflows, telemetry quality, coverage tracking, and alert context while reducing false positives. Partner with responders and security teams, conduct reviews, document operational content, and coach analysts and engineers.

Drive monthly financial close, forecasting, annual planning, and management reporting for Cloud Finance. Build financial models and scenario analyses, identify cost-saving opportunities, communicate insights to executives, automate finance processes, and analyze large datasets to develop metrics for Cloud and AI products. Partner closely with engineering, procurement, finance, governance, and go-to-market teams.

Architects and builds scalable agentic AI systems spanning runtimes, inference, evaluation, orchestration, distributed execution, ETL, HPC, cloud, Kubernetes, and GPU environments. The role optimizes reliability, performance, scalability, resource utilization, and developer experience; prototypes emerging technologies; writes production software; and provides technical leadership across research, engineering, product, and infrastructure teams.

Develop platform software using container runtimes and Kubernetes to enable GPU-accelerated applications. Design systems software for container orchestration, cloud-native environments, and distributed systems. Integrate software across NVIDIA engineering teams while addressing performance, security, reliability, and evolving priorities. The role involves Rust and Go development, Linux system internals, container sandboxing, confidential computing, virtualization, and GPU resource management.

Manage a multidisciplinary engineering team responsible for GSP firmware reliability, resiliency, field debugging, pre-silicon readiness, release execution, validation, and quality metrics. Partner with technical leaders on architecture, prioritization, and delivery while driving incident response, blocker resolution, test coverage, and robust GPU driver software releases across product lines.

Design, develop, test, and extend NVIDIA’s large-scale object storage platform. Responsibilities include improving availability, reliability, durability, fault tolerance, and performance across deployments exceeding 10,000 nodes and exabytes of data. The role also automates storage infrastructure provisioning, management, and monitoring; supports AI/ML research teams; owns product delivery through support; and contributes to open-source storage projects.

Lead technical engagement for AI training and infrastructure at scale. Collaborate with engineering teams and customers to ensure successful deployment and optimization of AI/ML solutions.

Develop and execute go-to-market strategies for NVIDIA’s networking products, coordinating Product, Sales, Marketing, and external partners. Create sales enablement materials, training, launch plans, partner co-marketing assets, communications, and product demonstrations. Translate complex networking technologies into clear positioning for technical and executive audiences, manage sales content repositories, and align cross-functional teams to deliver successful product launches.