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

Architect and ship large-scale ML and evaluation systems for Atlassian Rovo Chat. Define technical strategy for measuring helpfulness, correctness, reliability, and agentic capabilities; develop datasets, simulations, automated judges, experimentation platforms, and production quality signals. Identify failure modes, implement interventions, and measure customer impact. Lead cross-functional initiatives, establish reusable AI quality standards, and mentor engineers while balancing quality, safety, latency, reliability, and cost.

Lead end-to-end product design for Jira, integrating AI, Rovo, and agentic workflows into modern work management experiences. Set design vision, mentor designers, coordinate interconnected projects, collaborate cross-functionally, define success metrics, conduct research, and deliver cohesive, customer-centered solutions across Atlassian’s platform.

Lead adoption of Atlassian’s Teamwork Graph platform by developing positioning, messaging, go-to-market strategy, onboarding programs, enablement, and activation initiatives. Translate complex platform, data, and AI concepts for technical and business audiences, collaborate with Product, Engineering, Sales, and Customer Success, measure adoption analytics, optimize engagement programs, and represent the platform externally.

Own product marketing and developer go-to-market strategy for Atlassian Forge and its broader developer platform. Lead positioning, messaging, launches, adoption, developer growth, community programs, and the TEAM for Builders event. Translate technical capabilities such as APIs, AI workflows, MCP, and agents into compelling developer value. Partner with Product, Engineering, Advocacy, Events, Community, Growth, and Ecosystem teams while managing AWS partner funding and driving developer activation, Forge adoption, and Marketplace outcomes.

Develop and deploy scalable machine-learning solutions, particularly deep-learning forecasting models for company top-line metrics. Lead the full ML lifecycle from problem definition and experimentation through production deployment and improvement. Design production-grade architectures, evaluate and improve models, collaborate with cross-functional teams, explain technical concepts to varied audiences, and mentor junior engineers.

Leads and coaches Strategic Account Executives to exceed revenue targets, improve pipeline discipline, forecast accurately, and close complex SaaS opportunities. Acts as a player-coach in strategic demos and negotiations, analyzes funnel performance, develops underperforming representatives, and drives GTM improvements through customer feedback. The role also owns sales training, recruiting, onboarding, and building a high-performing team culture.

Serve as strategic legal counsel to product and engineering teams on AI/ML, data governance, intellectual property, regulatory, and platform issues. Develop scalable legal frameworks and launch checklists for AI product innovation, act as the primary legal partner for product initiatives, collaborate with legal subject-matter experts, and help build the Product Counsel function’s operating model and culture.

Supports product, design, and engineering teams by researching customer needs, competitors, market trends, and AI tools. The intern will translate insights into feature plans, identify product opportunities, build prototypes, collaborate with customers and partners, and help create innovative product solutions. The role requires full-time enrollment in a technical degree program and availability for a 40-hour weekly internship in the Pacific Time Zone.

Principal Machine Learning Engineer responsible for setting technical direction and building production-grade AI systems for Confluence. The role spans model evaluation, retrieval, ranking, prompt and workflow design, experimentation, reliability, and customer-facing AI product development. Responsibilities include making architectural decisions, partnering across engineering and product teams, mentoring engineers, identifying model and product failure modes, and delivering scalable AI experiences across content creation, editing, discovery, recommendations, and multimodal interaction.

Design, build, and operate scalable, low-latency search and retrieval systems powering Atlassian products. Develop and serve production ML models (neural rankers, embeddings, rerankers) using PyTorch and Triton. Implement vector indexing and RAG/agentic retrieval pipelines, ensure observability and cost optimization, manage on-call/runbooks, and collaborate cross-functionally while mentoring junior engineers.