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

Build and operate scalable machine learning systems for personalized recommendations, growth journeys, cross-product expansion, and sales experiences. Design models, datasets, features, evaluation frameworks, and decisioning services; run offline policy evaluations and online experiments; and monitor attribution, latency, quality, and fairness. Partner with product, engineering, data science, analytics, marketing, and sales teams to turn ambiguous growth opportunities into measurable production capabilities and strategic ML initiatives.

Paid 12-week software engineering internship involving coding and shipping product features, full-lifecycle development, technical training, mentorship, and collaboration on Atlassian cloud and core products. Interns will apply programming, data structures, algorithms, and software design fundamentals while working with senior engineers. Preferred experience includes prior software engineering projects or internships, AI/ML exposure, and familiarity with design patterns.

Lead backend architecture and deliver reusable solutions, unblock engineering teams across the development lifecycle, drive complex design and bug fixes, set code review standards, mentor engineers, collaborate on company-wide projects, and make technical decisions balancing correctness, performance, and customer impact.

Lead the technical direction for Atlassian’s Search Serving platform, building globally scalable, low-latency agentic search systems. Own architecture for retrieval, indexing, query processing, and ML inference while improving quality, reliability, scalability, operability, and cost. Establish production ML practices for evaluation, experimentation, observability, rollouts, and incident response. Optimize inference for embedding, retrieval, and ranking models, and lead cross-team initiatives while mentoring senior engineers and aligning technical leaders.

Own financial planning, forecasting, budgeting, and modeling for Atlassian’s AI infrastructure and token spend. Partner with Engineering, Product, Business Operations, and Finance leadership to evaluate AI economics, usage, vendor pricing, infrastructure requirements, COGS, unit economics, and gross margins. Drive scenario analysis, long-range planning, process improvements, and scalable financial models while translating complex technical and financial data into actionable executive insights.

Develop and execute executive social content focused on AI and product narratives across X, LinkedIn, and other platforms. Partner with executives, communications, marketing, and operations teams to shape voice, monitor AI discourse, translate technical concepts into engaging content, establish publishing cadences, advise on public participation, and measure performance to refine strategy.

Own and grow a named strategic account portfolio: develop and execute account/territory plans, build C-level relationships, identify upsell/cross-sell opportunities, lead complex negotiations, coordinate cross-functional teams and partners, provide forecasts, mentor junior sellers, and travel to meet customers and attend events.

Build an AI-powered marketing insight workflow using Rovo and Atlassian data. Responsibilities include developing agents to analyze OKRs, detect trends, draft executive summaries, flag low-performing results, generate leadership prompts, and create CMO pre-reads. The intern will establish human review and quality assurance processes, document reusable workflows, explore related automations, and measure time savings, adoption, and decision-making impact.

Leads outbound product strategy for Atlassian Service Collection’s enterprise go-to-market plays. Converts customer, competitive, security, compliance, and field evidence into product priorities, operating plans, executive recommendations, playbooks, and scalable mechanisms. Aligns Product, Engineering, Sales, Marketing, Security, Legal, Finance, and Customer Success stakeholders without formal authority, while supporting competitive displacement, enterprise transformation, trust readiness, and customer adoption.

Lead data science efforts to inform product and business strategy through quantitative analysis, experimentation, forecasting, and storytelling. Partner with cross-functional teams to define hypotheses, measure impact, influence roadmaps, present insights to senior leadership, and monitor key product metrics to drive value.