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

Own the data marketplace product lifecycle, improving vendor onboarding, submissions, quality feedback, offerings management, and buyer discovery. Partner with engineering, research, GTM, and operations to convert complex requirements into reliable workflows. Define marketplace health and adoption metrics, use qualitative and quantitative insights to prioritize product improvements, and guide launches and iteration in a fast-growing AI startup.

Business Operations generalist supporting hiring, people operations, finance, vendors, cross-functional projects, content, events, and special initiatives. The role partners closely with founders and the Chief of Staff, builds scalable operating systems, handles ambiguous business questions, and uses AI to automate workflows. It is an early-career, fast-paced position at an AI infrastructure startup requiring strong organization, communication, judgment, and adaptability.

Lead end-to-end recruiting for engineering and go-to-market roles at an early-stage AI infrastructure startup. Responsibilities include building recruiting operations and tooling, partnering with founders on role and interview design, sourcing exceptional candidates, evaluating technical and product-market aptitude, supporting onboarding, and helping establish performance reviews, leveling, and feedback systems.

Own HUD’s outbound commercial pipeline end to end, including targeting, enrichment, outreach, qualification, follow-up, and meeting booking. Build scalable GTM workflows, CRM automations, lead scoring, and custom AI-powered tools. Develop messaging for ML engineers, AI researchers, and engineering leaders, close deals, analyze funnel performance, and collaborate with founders to refine the GTM playbook.

Own HUD’s outbound business development pipeline from targeting and enrichment through outreach, follow-up, qualification, and meeting booking. Build scalable GTM workflows, CRM automation, lead scoring systems, and custom AI-powered tools. Develop technical messaging for ML engineers, AI researchers, and engineering leaders, track pipeline metrics, optimize conversions, and collaborate with founders to refine the GTM playbook. The role also involves closing deals and rapidly learning prospect industries and technical workflows.

HUD is accepting applications for exceptional candidates across research, engineering, go-to-market, and operations. Applicants must propose a specific contribution based on research into HUD and provide concrete evidence of exceptional ability and relevant experience. The role is full-time, based in San Francisco, Singapore, or remotely with substantial time-zone overlap. The hiring process includes two technical interviews and a two- to three-day work trial.

Build privacy and anonymization systems that detect and transform PII, credentials, secrets, and other sensitive information in data used for AI training. Develop detection methods combining rules, statistical models, classifiers, and LLMs; create production data-processing pipelines; evaluate privacy risk and retained utility; and ensure robustness against schema drift, unusual formats, leakage, and adversarial re-identification.

Own HUD’s production infrastructure and backend platform systems, focusing on reliability, scalability, performance, cost, deployment safety, and developer experience. Build AWS infrastructure with Terraform and Kubernetes, improve CI/CD and observability, design resilient services and asynchronous systems, manage incidents, and develop internal automation and tooling. The role supports high-availability AI, marketplace, and data-heavy workloads.

Lead HUD's data quality strategy for RL training data: build QC systems, validate synthetic data at scale, design evaluation experiments, partner with researchers and vendors, convert research insights into production validation pipelines and tools, and mentor research engineers.

Build full-stack product surfaces, backend services, APIs, databases, dashboards, internal tools, vendor workflows, and observability for reinforcement learning data and evaluation systems. Develop interfaces for browsing environments, inspecting trajectories, reviewing quality, debugging failures, and understanding model behavior. Partner with research, operations, vendors, and go-to-market teams to translate ambiguous requirements into reliable products. The role also involves production debugging and cloud infrastructure collaboration.