Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Design, develop, and optimize high-performance C++ systems for ultra-low-latency media streaming. Build and improve streaming pipelines (WebRTC, GStreamer, RTP/RTCP), analyze performance across server, network, and media pipelines, and collaborate with encoding, cloud networking, and systems teams to define KPIs and reduce latency for cloud gaming and related use cases.

Design and implement Rust-to-GPU compiler backends, IR frameworks, and JIT/AOT toolchains. Lower Rust AST/MIR to MLIR/LLVM/PTX, build GPU-specific optimizers, enforce safe parallel abstractions across host/device boundaries, and expose ergonomic device-side primitives. Drive projects from research to production and support profiling, debugging, and benchmarking workflows.

Architect, prototype, and deploy production-grade agentic and multi-agent AI systems for Media & Entertainment. Build high-performance RAG pipelines over multi-modal assets, optimize GPU inference and TCO on NVIDIA platforms, advise customers pre/post-sale, and create reusable reference architectures, blueprints, and technical collateral to scale solutions.

The role involves driving the adoption of NVIDIA Omniverse, collaborating with partners on technical solutions, and providing support to overcome integration challenges while engaging at all levels.

The role involves researching and developing GPU-accelerated AI workloads, optimizing algorithms, and collaborating with technical experts to improve hardware performance.

Lead strategic GTM and co-selling initiatives with Google Cloud to drive NVIDIA AI adoption. Develop and maintain technical enablement assets, run developer activations and events, collaborate with Google Cloud field and engineering teams, convert samples into demos and labs, and translate customer feedback into product and roadmap insights.

Design and validate Ethernet networking fabrics for AI/HPC clusters; mentor customers; build demos/POCs; create reference architectures and training; collaborate with engineering, product, and sales to secure design wins and drive product feature development.

Serve as a technical advisor for semiconductor customers and EDA developers to GPU-accelerate EDA and manufacturing workflows, apply ML/DL for inspection and yield, analyze architectures for acceleration, deliver trainings/demos, and collaborate with product and engineering teams to deploy scalable AI-enabled solutions.

Design and optimize high-performance C++/CUDA libraries for GPU-based data processing. Improve multi-node GPU communication (UcxExchange), optimize Presto GPU query performance, and lead enhancements across Presto, Velox, and cuDF projects for efficient DataFrame and database acceleration.

Design and implement an MLIR-based AI compiler for GPU inference: develop graph representations and dialects, optimize performance and memory, define APIs, generate kernels, and collaborate with framework and hardware teams to enable new model patterns and GPU features.