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As a Principal Machine Learning Systems Engineer, you will lead the development and implementation of machine learning systems and solutions to enhance team collaboration and productivity across Atlassian's software products.

Build a cross-platform endpoint agent to detect and attribute AI-generated code at the line level, design pipelines to capture/classify/store developer and agent activity, work with Git internals, integrate with coding agents, and ensure reliable, invisible production deployments across macOS, Linux, and Windows.

Drive Atlassian's Apps Marketplace partner strategy by building strategic partner plans, managing ISV relationships, coordinating cross-functional stakeholders, guiding technical requirements, and executing joint GTM and revenue programs. Support enterprise deal pipelines, contract negotiations, and partner success tracking with data-driven metrics.

Drive net-new enterprise sales across AMER and LATAM accounts. Partner with SDRs, manage proofs of concept, build business cases, and close deals while establishing DX as a strategic, org-wide solution. Focus on accounts with 100–750 engineers and work closely with cross-functional teams to ensure customer success and expansion.

Sell DX/Atlassian SaaS to AMER and LATAM accounts: prospect, qualify, run demos and POCs, own full sales cycle, close net-new and expansion deals, forecast pipeline, and translate customer feedback into product and GTM improvements.

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.

Set technical vision and build AI foundations used across Atlassian: prototype and write code, build scalable AI infrastructure, measure AI effectiveness, provide context and cost-efficient compute, partner with product teams, and mentor senior engineers.

Partner with Enterprise Account Executives to generate and qualify pipeline for large, complex customers through outbound prospecting (email, social, video, calling). Meet setting, conversion, and quota goals while collaborating with sales, marketing, partners, and operations. Use sales tools (SFDC, Gong, Outreach, LinkedIn Navigator) and develop deep customer understanding.

Partner with Sales and Success Account Teams to build pipeline through outbound prospecting, meeting-setting, and quota attainment. Craft value-driven messaging, email and video outreach, handle objections, and use SFDC, Gong, Outreach, and LinkedIn Navigator to track activity and conversions.

Lead and grow an engineering team building enterprise-grade agentic AI integrations. Define technical strategy for multi-agent systems (MCP, A2A), collaborate cross-functionally, maintain highly reliable, low-latency services, and drive industry-spec contributions and interoperability.