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

Build and manage the operating system for paid creative and advertising programs. Coordinate creative, marketing, agency, and production teams; manage briefs, assets, approvals, trafficking, launches, reporting, and iteration. Develop testing roadmaps, track performance, document learnings, and improve workflows, automation, templates, and reporting to increase creative throughput and campaign reliability.

Own financial models, budgets, forecasts, reporting, and investment tracking for AI infrastructure and semiconductor programs. Translate silicon milestones, cost drivers, and infrastructure needs into pricing and investment recommendations. Evaluate customer and partner economics, support deal negotiations, and present financial insights and risks to engineering, research, commercial, and finance stakeholders.

Design and execute rigorous assessments of frontier AI risks with external evaluators and internal teams. Build evaluation infrastructure, secure assessment environments, auditing tools, and reproducible experiments. Analyze model capabilities and safeguards, reproduce external findings, investigate discrepancies, test mitigations, and clearly document evidence, limitations, and uncertainty for technical and organizational decision-makers.

Lead OpenAI’s global education go-to-market strategy across higher education, country partnerships, and K-12. Manage and scale Account Directors and regional leaders, oversee major enterprise opportunities, build executive and institutional relationships, and establish repeatable sales, partnership, forecasting, adoption, and expansion practices. Partner cross-functionally to connect AI capabilities with customer deployment, educator enablement, sustained usage, and value realization.

Develop and execute scientific data programs supporting AI research, especially in biology and chemistry. Translate research needs into data strategies, identify and evaluate scientific data sources, build external partnerships, negotiate commercial terms, coordinate legal, privacy, security, procurement, and engineering stakeholders, and oversee data delivery and research use. Improve sourcing and delivery through code, APIs, automation, and AI tools while managing sensitive, ethical, and privacy considerations.

Partners with leaders to shape organizational structures, talent plans, performance and retention programs, manager capability, and employee experience. Diagnoses complex people and organizational challenges, advises on consequential decisions, leads change, and develops scalable HR solutions in a high-growth environment.

Develop and productionize machine learning classifiers and evaluation systems for assessing model behavior. Responsibilities include supervised fine-tuning, distillation, rigorous evaluation, inference optimization, deployment, observability, reliability, and ongoing improvement. The role partners with researchers and software engineers to translate open-ended research needs into measurable ML objectives and scalable production systems, balancing model quality, latency, compute utilization, and cost.

Own compensation tools, HRIS modules, datasets, dashboards, audits, and data flows. Manage annual and off-cycle compensation cycles, vendor relationships, data quality, testing, troubleshooting, and process improvements. Partner with People Systems, Engineering, Finance, Total Rewards, and vendors to maintain accurate, scalable, and audit-ready compensation infrastructure.

Leads communications and community engagement for data center development, including public meetings, press and storytelling, stakeholder narratives, social media campaigns, media preparation, policy communications, and cross-functional alignment. The role connects permitting, policy, communications, and project teams while shaping energy policy approaches and public perception of infrastructure initiatives.

Design, build, and operate backend infrastructure and shared harnesses for AI agents. Create reusable capabilities for context, tool use, memory, orchestration, identity, observability, and safe execution. Collaborate with research and product teams to turn research into dependable production services optimized for reliability, cost, and latency.