M60 Senior Machine Learning Manager, Gen AI & Knowledge AI

Working at Atlassian

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At Atlassian, we're on a mission to unleash the potential of every team. As part of that mission, we're investing deeply in GenAI products intelligent, conversational systems that combine retrieval-augmented generation (RAG), entity / user understanding, semantic search, and agentic interactions to redefine how users discover and interact with information across our ecosystem.

We're seeking a Senior Machine Learning Manager (M60) to lead a cross-functional initiative focused on delivering end-to-end GenAI products. This is a rare opportunity to drive innovation at the intersection of modeling, engineering, and product thinkingturning state-of-the-art LLM capabilities into intuitive, high-value user experiences.

🧠 Own End-to-End GenAI Product Development

• Lead the vision, design, and execution of LLM-powered AI products, leveraging advance AI modeling (e.g. SLM post-training/fine-tuning), RAG architectures and hybrid ranking system.
• Define system architecture across retrievers, rankers, orchestration layers, prompt templates, and feedback mechanisms.
• Work closely with product and design teams to ensure delightful, fast, and grounded user experiences.

• Build and manage a cross-disciplinary team including ML engineers, backend/frontend engineers, and applied scientists.
• Foster a culture of E2E ownership empowering the team to move from prototype to production quickly and iteratively.
• Mentor individuals to grow in both technical depth and product acumen.

• Shape the technical roadmap and long-term strategy for GenAI search across Atlassian's product suite.
• Partner with platform and infra teams to scale inference, evaluate performance, and integrate usage signals for continuous improvement.
• Champion data quality, grounding, and responsible AI practices in all deployed features.

• 8+ years in ML, search, or backend engineering roles, with 3+ years leading teams.
• Strong track record of shipping ML-powered or LLM-integrated user-facing products.
• Experience with RAG systems (vector search, hybrid retrieval, LLM orchestration).
• Deep experience in either modeling (e.g., LLMs, search, NLP) or engineering (e.g., backend infra, full-stack), with the ability to lead end-to-end.

• Deep understanding of LLM ecosystems (OpenAI, Claude, Mistral, OSS), orchestration frameworks (LangChain, LlamaIndex), and vector databases (Weaviate, Pinecone, FAISS, etc.).
• Strong product intuition and ability to translate complex tech into valuable user features.
• Familiarity with GenAI evaluation methods: hallucination detection, groundedness scoring, and human-in-the-loop feedback loops.

• Master's or PhD in Computer Science, Machine Learning, or related field preferredor equivalent practical experience.

• Experience with front-end or full-stack development for GenAI interfaces.
• Familiarity with knowledge graphs, semantic embeddings, or search evaluation metrics (e.g., NDCG, precision@k).
• Passion for AI safety, ethics, and user trust in generative systems.