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

Own full-cycle recruiting for specialized hardware engineering roles, including ASIC, RTL, verification, physical design, DFT, and silicon validation. Partner with engineering leaders to define hiring needs, source passive candidates, manage interviews, close experienced engineers, and improve recruiting operations. Use pipeline data, recruiting tools, LLMs, and agentic AI to increase efficiency while maintaining human-centered candidate engagement.

Optimize end-to-end LLM inference performance on custom FPGA and ASIC accelerators. Responsibilities include profiling latency and throughput, tuning hardware-accelerated attention, optimizing distributed inference and KV cache data movement, building benchmarks and regression tests, and debugging across applications, runtimes, drivers, firmware, and hardware. The engineer will also influence accelerator architecture based on measured performance and support production deployments.

Designs and implements Go-based control-plane software for custom AI inference hardware. Responsibilities span system management APIs, Linux services, hardware interfaces, diagnostics, observability, security, firmware updates, distributed systems, and workload management. The role requires hands-on debugging across application, operating system, firmware, and hardware boundaries, plus technical leadership, mentoring, architecture, and production ownership. Regular on-site lab work includes server racking, system bring-up, hardware troubleshooting, and documentation.

Define multi-generation hardware/software architectures for AI inference silicon, including boot, memory, DMA, scheduling, security, telemetry, and host/device interfaces. Integrate firmware, Linux, runtimes, simulation, and developer tooling; lead architecture reviews; resolve cross-team technical issues; prototype high-risk interfaces; and mentor senior engineers. The role requires deep systems expertise, strong C or C++ skills, and architecture-level ownership across pre-silicon, silicon bring-up, and production software.

Build simulation and modeling infrastructure for AI inference accelerators before silicon availability. Develop analytical performance models, functional and timing simulators, virtual platforms, and software integrations. Validate functionality and performance against reference implementations and hardware, improve simulator speed and usability, and create automated regression coverage. Collaborate with hardware, compiler, firmware, runtime, performance, and test engineers to support architecture exploration and realistic software workloads.

Owns AI-assisted engineering across accelerator software teams, embedding agentic workflows into coding, debugging, triage, testing, documentation, CI, and silicon-debug processes. Builds shared agent tooling, maintains evaluations, measures productivity gains, and establishes standards for reliable AI-native development. Requires expert experience with Codex, Claude Code, LLM APIs, evaluations, and adopted developer tooling, plus strong systems or infrastructure credibility.

The Senior Software Engineer will develop high-performance software for executing open-source LLMs on custom hardware, focusing on optimizations and efficient libraries primarily in C++.