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

Build and lead production machine learning and evaluation systems for Atlassian’s Rovo AI agents. Responsibilities include developing datasets, simulations, automated judges, experimentation platforms, and quality signals; advancing reasoning, planning, retrieval, memory, and tool use; identifying and correcting systemic failures; and balancing quality, safety, latency, reliability, and cost. The role requires hands-on technical leadership, cross-functional influence, architectural decision-making, mentorship, and delivery of measurable customer outcomes at scale.

Manage a book of ~40 mid-market accounts to drive new sales and expansion. Own quota ($2-4M), lead cross-functional deal teams, build executive relationships, qualify and close complex multi-threaded SaaS deals using outcome-based selling (MEDDPICC), forecast pipeline, price and negotiate contracts, collaborate with channel/marketing/product/customer success, and occasionally travel for customer meetings and team events.

Lead technical strategy and joint solution architecture with Accenture, designing enterprise AI, graph, integration, and cloud-platform offerings. Drive partner enablement through architecture sessions, hackathons, bootcamps, proofs of concept, and reference architectures. Collaborate with sales, alliances, product, and engineering teams on strategic enterprise transformations, while influencing platform capabilities and supporting co-delivery governance.

Summer 2027 PhD research internship at Atlassian focused on AI research. The role supports distributed-first collaboration, with virtual interviews and flexibility to work remotely or from a company office.

Conduct AI research as a 2027 graduate hire at Atlassian, collaborating remotely or from company offices. The provided description contains no specific research area, responsibilities, qualifications, or technical requirements.

Principal Software Engineer leading frontend and full-stack architecture for Atlassian’s Growth Platform. The role defines technical strategy for messaging, personalization, notifications, and experimentation systems; designs scalable microservices, APIs, data models, and React/TypeScript components; drives performance, reliability, security, accessibility, observability, and CI/CD standards; and influences multiple teams through architecture reviews, cross-functional partnership, and mentorship.

Lead enterprise customer engagements to adopt and operationalize Atlassian’s Rovo AI platform. Facilitate discovery, design and configure AI agents and connectors, guide deployments from sandbox to production, and drive measurable adoption outcomes. Own engagement playbooks, enable internal teams, provide product feedback, and collaborate with sales, customer success, product, and engineering teams. The role involves supporting complex enterprise use cases, mentoring without direct authority, and up to 15% domestic and international travel.

Principal Software Engineer responsible for owning customer-facing products end to end, leading technical strategy, architecting large-scale distributed systems, and delivering AI-native experiences. The role involves full-stack implementation, product strategy, system design, code reviews, reliability, testing, telemetry, security, performance, and mentoring engineers. Candidates must have extensive experience building and operating large-scale systems and strong product, UX, communication, and collaboration skills.

Lead Atlassian’s TDP SQL engineering team, owning people leadership, platform strategy, roadmap execution, reliability, security, compliance, incident response, technical direction, cross-functional dependencies, infrastructure economics, and operational outcomes. Build a high-performing organization and evolve a scalable managed relational database platform across multiple cloud environments while supporting enterprise requirements such as data residency, encryption, disaster recovery, and tenant isolation.

Lead machine learning architecture and technical direction across multiple quarters while remaining involved in experimentation, quality, and production systems. Work across model post-training, personalization and recommendation, and agentic experiences. Define solutions for ambiguous problems, align cross-functional partners, drive execution, and safely bring ML systems into production.