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

Build and ship polished, high-performance interfaces for the Codex desktop app and IDE extension. Partner with design to develop interaction models for AI-agent workflows, including delegation, timelines, progress, handoffs, review, and controls. Own frontend architecture, component systems, state management, navigation, rendering performance, testing, instrumentation, and reliability while collaborating with platform, model, and backend teams.

Leads execution of pricing and packaging changes from approval through launch. Translates pricing strategies into requirements for billing, quoting, revenue, and related systems; manages cross-functional dependencies, prioritization, testing, governance, documentation, and launch readiness. Partners with Product, Engineering, Billing, Finance, and GTM teams to improve pricing workflows, reduce ambiguity and rework, and ensure scalable operational execution.

Lead the Applied AI Architect team and oversee customer engagements from pre-sales discovery through deployment and adoption. Develop team strategy, guide complex technical solutions, manage executive customer relationships, support generative AI and machine learning sales, and coordinate with Sales, Product, Engineering, Research, and Deployment to deliver measurable customer outcomes.

Build and scale foundational data infrastructure for AI-powered hardware and consumer devices. Responsibilities include developing reliable pipelines, integrating manufacturing, telemetry, quality, and customer operations data, creating durable data models, enabling manufacturing and product intelligence, and establishing data engineering standards. The role requires strong architectural judgment, distributed systems knowledge, production infrastructure experience, and cross-functional collaboration in an early-stage organization.

The role focuses on strategic enterprise sales development, engaging with stakeholders, shaping deals, and nurturing enterprise opportunities for AI solutions.

The Data Scientist will turn ambiguous B2B and go-to-market questions into actionable insights for strategy, monetization, product adoption, customer segmentation, field deployment, and organizational design. Responsibilities include analyzing product, customer, sales, revenue, and market data; partnering with cross-functional leaders; communicating recommendations to executives; applying AI tools; and helping establish the operating model for an emerging B2B data science team.

Leads integrated marketing programs showcasing AI’s impact through human-centered stories. Discovers and develops customer, community, and societal proof points; partners with Social, Community, Communications, Creative, and agencies; and manages briefs, production, budgets, launches, distribution, and measurement across channels. Builds scalable storytelling programs, local and national partnerships, and audience participation initiatives while ensuring credibility, quality, and thoughtful representation.

Build and operate classification and evaluation platforms, including software services, APIs, online and offline pipelines, and model-serving systems. Improve inference performance, reliability, observability, and incident response while creating experimentation and debugging tools. Partner with machine learning engineers, researchers, and data scientists to productionize classification capabilities, understand user workflows, and shape the platform’s architecture and product direction.

Advise product, engineering, design, and business teams on legal issues throughout hardware and software product development, launch, and support. Responsibilities include software and firmware licensing, open-source compliance, consumer terms, disclosures, marketing claims, privacy, security, intellectual property, competition, regulatory requirements, and connected device services. The role requires translating technical and legal complexities into practical guidance while building cross-functional processes and managing emerging product issues.

Build and improve developer infrastructure for ChatGPT services, including local development, Bazel builds, CI, deployment pipelines, test workflows, and production observability. Reduce development and deployment latency, improve reliability, automate pull-request and deployment remediation, and establish metrics for workflow performance. The role requires debugging distributed systems, coordinating ownership across teams, and balancing engineering velocity with production safety.