Atlassian is investing deeply in GenAI products, aiming to redefine how users discover and interact with information across their ecosystem. They are seeking a Senior Machine Learning Manager to lead the development of end-to-end GenAI products, integrating state-of-the-art LLM capabilities into user experiences.
Requirements
- 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.)
- Familiarity with GenAI evaluation methods: hallucination detection, groundedness scoring, and human-in-the-loop feedback loops
- 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
Responsibilities
- Lead the vision, design, and execution of LLM-powered AI products
- Define system architecture across retrievers, rankers, orchestration layers, prompt templates, and feedback mechanisms
- Leveraging advance AI modeling (e.g. SLM post-training/fine-tuning), RAG architectures and hybrid ranking system
- 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
- Build and manage a cross-disciplinary team including ML engineers, backend/frontend engineers, and applied scientists
- Shape the technical roadmap and long-term strategy for GenAI search across Atlassian’s product suite
Other
- 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.
- Work closely with product and design teams to ensure delightful, fast, and grounded user experiences.
- 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.
