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

Owns reliability of large-scale GPU network fabrics supporting AI training and inference. Responsibilities include validating fabric designs, debugging RDMA/RoCEv2, NCCL collectives, PFC/ECN, NVLink/NVSwitch, and host-level networking; building instrumentation, dashboards, and alerts; troubleshooting across NICs, drivers, kernels, switches, and workloads; and driving end-to-end escalations with cloud networking providers.

Build and scale infrastructure supporting frontier AI model training, research, and product development. Responsibilities may include operating Kubernetes clusters with GPU workloads, developing data pipelines with Spark, improving developer productivity tooling, debugging across application, operating system, and network layers, and delivering reliable distributed systems end to end.

Build and ship AI-powered products from prototype to production. Develop full-stack web features, including backend services, APIs, and polished frontend interfaces. Collaborate with researchers, product managers, and designers to translate model capabilities into usable experiences. Own technical architecture, scalability, reliability, and maintainability while making pragmatic tradeoffs in ambiguous environments. Help establish engineering practices and standards for a growing product engineering team.

Design, optimize, and scale infrastructure for high-performance AI model inference. The role develops distributed serving systems, improves latency, throughput, reliability, observability, and hardware utilization, and collaborates with research teams to deploy novel architectures. Responsibilities include extending Kubernetes, Ray, and SLURM orchestration, optimizing GPU-based systems, supporting evaluations and production deployments, and sharing infrastructure advancements through documentation, open-source work, or technical reports.

Advance agentic AI capabilities by building synthetic data frameworks, developing task environments for tool use and long-horizon workflows, designing training data and recipes, conducting rigorous ablation experiments, and improving model usability through evaluations and feedback. The role requires strong software engineering, deep learning, distributed training, and scientific experimentation skills, with substantial autonomy across the research and development lifecycle.

Design, implement, and optimize distributed training infrastructure for large-scale AI models across thousands of GPUs and nodes. Build high-performance frameworks and libraries that improve throughput, reliability, reproducibility, and scalability. Collaborate with researchers and engineers, establish system standards, debug complex codebases, and share advances through documentation, open-source projects, or technical reports.

Source and engage exceptional research, engineering, and infrastructure candidates for an AI startup. Build technical talent pipelines, develop personalized outreach, partner with recruiters and hiring managers, track sourcing metrics, represent the company’s mission, and help create scalable sourcing processes and infrastructure.

Build and maintain research infrastructure, including evaluation frameworks, training systems, experiment tracking, data pipelines, and visualization tools. Partner directly with researchers to identify bottlenecks, improve productivity, and deliver reliable end-to-end systems. Develop reproducibility, traceability, quality control, monitoring, and observability capabilities while treating research tooling as a product.