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

Develop and lead high-performance Lustre features across kernel modules, user-space utilities, and storage subsystems. Improve scalability, metadata performance, I/O, locking, RDMA, and cloud capabilities for multi-petabyte environments. Drive design, coding, testing, profiling, performance tuning, and upstream contribution. Evaluate emerging storage technologies, strengthen validation tooling, collaborate with hardware and application teams, mentor developers, and contribute technical documentation and reviews.

Leads worldwide tax provision, consolidated U.S. federal tax compliance, international tax planning, audits, and tax strategy. Oversees ASC 740 reporting, GILTI, FDII, BEAT, foreign tax credits, controls, process modernization, and executive communication. Partners with finance, legal, treasury, FP&A, auditors, and external advisors while developing the U.S. tax team and supporting acquisitions, restructurings, financing, and entity optimization.

Designs, builds, and optimizes NFS and distributed storage systems. Diagnoses performance and reliability issues across kernel-space and user-space I/O, file systems, storage media, networking, and platform layers. Develops Python automation for observability, testing, debugging, and operations. Requires deep hands-on expertise with NFS, distributed file systems, object storage, Kubernetes, and high-performance technologies including NVMe, SSDs, RDMA, and high-speed networking.

Leads hands-on design, development, and optimization of AI data movement and distributed storage systems. Responsibilities include integrating NVIDIA NIXL and DDN Infinia with GPU inference platforms, optimizing GPU-to-storage I/O using GPUDirect Storage, RDMA, and NVMe-over-Fabrics, developing KV cache and multi-tier storage strategies, benchmarking production systems, resolving performance bottlenecks, influencing distributed inference architecture, and mentoring engineers.

Designs and optimizes FUSE-based file systems, Linux kernel and user-space integrations, distributed and object storage infrastructure, and high-performance networking using RDMA. The role develops systems tooling in C and C++, diagnoses performance bottlenecks across storage, networking, memory, and I/O layers, and builds scalable infrastructure for AI, HPC, and data-intensive production environments.

Build and optimize production LLM serving and inference systems, improving GPU and CPU performance, memory usage, KV caching, storage, throughput, and latency. Design scalable infrastructure for RAG and retrieval-heavy workloads while solving distributed systems challenges across compute, memory, and storage. The role requires hands-on ownership of AI infrastructure and the ability to balance architecture decisions with implementation in performance-critical environments.

Leads end-to-end security architecture for distributed storage platforms and AI-driven workloads. Responsibilities include threat modeling, SSDLC, IAM integration, RBAC and ABAC authorization, encryption and key management, multi-tenant isolation, secure APIs and protocols, observability, anomaly detection, compliance, and cross-functional technical leadership. The role partners with storage, protocol, control-plane, and platform engineering teams while mentoring engineers and influencing secure system design.