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

Own and evolve the company’s visual identity across web, campaigns, social, sales collateral, events, and merchandise. Translate complex voice AI concepts into clear diagrams, illustrations, and marketing graphics. Build scalable brand systems, templates, and standards while partnering with product, marketing, and executive teams. Execute hands-on across digital, print, and experiential media with limited direction and high craft.

Own end-to-end data platform architecture across ingestion, transformation, modeling, and serving. Improve warehouse efficiency, access control, reliability, observability, orchestration, CI/CD, and developer experience. Lead cross-functional technical initiatives, build monitoring and alerting, mentor engineers, guide architectural decisions, and develop AI-powered data tooling. The role requires strong SQL and Python skills, production experience with Airflow, BigQuery, and containerized infrastructure, plus architectural ownership and stakeholder communication.

Design and build backend systems for finance and accounting modules within an AI-native ERP platform. Responsibilities include modeling invoicing, payments, and financial reporting data; developing Python and FastAPI services with PostgreSQL; building integration APIs; aggregating operational and financial data; and owning greenfield features from design through deployment. The engineer will primarily own backend systems while contributing to React frontend development as needed.

Lead technical pre-sales for enterprise healthcare data and AI deals. Own discovery, solution design, RFPs, security reviews, proof-of-value programs, data-delivery scoping, and technical evaluations. Configure integrations with Snowflake, Databricks, APIs, and CRM systems; explain healthcare claims data and AI capabilities to diverse customers. Build demos, reusable enablement resources, and scalable architecture patterns while partnering with commercial, product, data, and engineering teams.

Own the company’s AI-powered quality and release infrastructure as its first dedicated hire. Build agent-run test harnesses, LLM graders, regression coverage, release gates, fresh-install and upgrade verification, and natural-language UAT. Manage CI health, artifact publication, deployment pipelines, credentials, certificates, and staging environments. Write production Python code, convert escaped bugs into regression tests, and provide trusted release-readiness evidence through dashboards, runbooks, retrospectives, and operating policies.

Own the full sales cycle for an AI-powered corporate compliance platform, from self-sourced prospecting and discovery through demonstrations, negotiation, and close. Sell to General Counsel, CFOs, Corporate Secretaries, and compliance leaders while maintaining pipeline, activity, conversion, and quota metrics. Partner with BDRs as inbound demand grows, continue self-sourcing approximately one-third of pipeline, and travel for prospect meetings and industry events.

Conduct original research on reliable agentic AI, developing methods for simulation, evaluation, optimization, regression control, and continuous learning. Analyze agent failures, traces, and human feedback; create experiments and prototypes; and translate research into production-facing systems. Collaborate with product and engineering teams, contribute to technical strategy, and provide rigorous experimental evidence. The role requires strong research depth, publication or open-source contributions, hands-on Python development, and expertise in LLM agents or agentic systems.

Own full-funnel growth and performance marketing for an enterprise B2B SaaS company. Responsibilities include managing paid media and ABM programs, building attribution and reporting infrastructure, optimizing campaigns, managing a seven-figure budget, maintaining the martech stack, and partnering with sales to drive pipeline, revenue, CAC, ROAS, and payback outcomes.

Own backend-heavy product and platform engineering from design through production. Build and scale distributed systems supporting large-scale mortgage transactions, design reliable infrastructure for regulated workflows, contribute to architecture and engineering standards, and collaborate with product, infrastructure, and platform teams. Use AI coding tools while maintaining rigorous human review and correctness.

Build end-to-end frontend and backend features for a voice AI trust platform while working directly with enterprise customers. Responsibilities include customer discovery, debugging, proofs of concept, product decisions, reusable platform development, and post-sales technical support. The role requires TypeScript and Python proficiency, AI coding tool fluency, strong product judgment, clear communication, and approximately 10%–20% travel.