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

The role involves enhancing AI system reliability, developing service objectives, monitoring infrastructure, leading incident responses, and collaborating across teams.

As a Research Engineer, you'll enhance AI systems by optimizing model training, debugging across multiple layers, designing experiments, and maintaining operational reliability during model launches.

The Performance Engineer will analyze system performance, evaluate model outputs, build observability tools, and partner with teams to optimize throughput, latency, and correctness in AI systems.

Lead a team of Growth Account Executives to drive revenue growth and retention with startup customers, establish processes, and mentor team members.

Own end-to-end enablement for the Claude Developer Platform: build demos, hands-on labs, and self-service resources; deliver and scale live technical training; coach technical sellers; stress-test features with product; and measure content effectiveness to improve field readiness.

As an Engineering Manager, you'll lead efforts to enhance model performance, manage team projects, and support professional growth in a dynamic environment.

Own technical execution of live and hybrid events, serving as technical director for productions from intake through follow-up. Operate video switchers, audio consoles, cameras, encoders and streaming; build run-of-show and runbooks; perform preventative maintenance and room health checks; scope events, staff productions, coordinate contractors, and communicate technical status to nontechnical stakeholders.

Own end-to-end silicon program activities including specification, technology/IP selection, architecture tradeoffs, hardware-software co-design, and first-silicon bring-up/debug. Partner with external ASIC/IP/foundry partners, build AI-assisted design workflows, and drive directional technical decisions across chip domains from front-end to packaging and test.

Build, maintain, and improve algorithms and infrastructure for finetuning and RL training (RLHF) of large language models. Improve speed, reliability, and usability of training pipelines through profiling, instrumentation, debugging GIL contention, launching test jobs, and implementing research algorithms to support production and internal model training.

Lead system-level architecture for large-scale AI compute: define specs, evaluate vendor designs, drive interconnect, power, thermal, and reliability strategies, develop validation and bring-up plans, and partner with ML, infrastructure, and supply chain teams.