Top Tech Jobs & Startup Jobs in Los Angeles, CA

Research and develop GPU-acceleration techniques for high-performance databases, ETL, and data analytics applications. Analyze data-intensive workloads, optimize algorithms, memory management, and CUDA kernels, and investigate CPU/GPU bottlenecks. Collaborate with research, hardware, software, and industry teams to influence next-generation architectures, programming models, and data-processing solutions.

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.

Drive NVIDIA platform adoption across enterprise AI and semiconductor ecosystems. Develop and execute go-to-market strategies, build strategic partnerships, enable sales teams, create and close complex opportunities, grow revenue and pipeline, provide product feedback, and lead event strategy. The role requires deep AI/ML expertise, executive relationship management, solution architecture, business strategy, and up to 25% national travel.

The Senior LLVM Compiler Engineer will contribute to upstream LLVM development, lead design discussions, and ensure high-quality compiler solutions in collaboration with open-source communities.

Develop and implement core firmware features for Mellanox NICs supporting cloud, HPC, and storage networking. Collaborate with architecture and software teams, create verification tests, debug complex hardware/software systems, and contribute to firmware maintenance, qualification, and releases. The role requires strong C/C++ development, networking knowledge, PCIe and virtualization experience, scripting skills, and familiarity with distributed, high-performance computing environments.

Develop innovative physical design methodologies for GPUs and SOCs, focusing on PPA improvements and collaboration across multiple teams. Use AI/ML to enhance design efficiency.

Design, deploy, maintain, tune, and secure enterprise Linux environments supporting high-performance EDA compute farms. Lead infrastructure-as-code automation, configuration management, continuous deployment, and tooling development using Python, Bash, or Go. Manage LDAP/SSSD naming services, NFS and Autofs storage, virtualization, container infrastructure, registries, networking, and infrastructure telemetry. Support highly available, scalable systems and potentially integrate cloud platforms, CI/CD pipelines, EDA toolchains, and HPC clusters.

Develop Rust-to-GPU compiler pipelines, custom intermediate representations, optimizers, and ahead-of-time, just-in-time, and link-time compilation tooling for NVIDIA GPUs. Design Rust-based safety abstractions across GPU execution boundaries and expose low-level CUDA hardware features. Responsibilities include compiler architecture, GPU code generation, debugging, profiling, benchmarking, and independently driving complex engineering initiatives from research through production.

Design, deploy, operate, and automate reliable large-scale EDA infrastructure services on NVIDIA hardware. Build tools that reduce manual work, support distributed private and public cloud systems, define service-level objectives, improve observability, and participate in incident response and on-call rotations. The role also provides systems-design guidance to peer teams and may involve Kubernetes, OpenStack, Docker, Slurm, multi-cloud infrastructure, networking, storage, and GPU-based systems.

Leads a distributed team securing NVIDIA DGX Cloud’s large-scale GPU infrastructure. Responsibilities include hiring and developing engineers, setting strategy for security control planes and foundational services, delivering measurable risk reduction, maintaining engineering standards, communicating security risks to executives, and collaborating with platform, SRE, networking, and security teams. The role requires strong engineering management, infrastructure or security expertise, cloud-native fluency, and influence across partner organizations.