Senior Machine Learning Engineer, AI-Native Evaluation & Search

Engineering | San Francisco, United States | Remote, Remote | Mountain View, United States or Remote | Seattle, United States



Atlassian is looking for a Senior Machine Learning Engineer to join our Search & Intelligence organization. We build AI-native experiences, agentic systems, models, evaluation frameworks, and data platforms that power Atlassian's AI products.

Working at Atlassian

Atlassians can choose where they work whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity.

Your role and impact

You will develop and productionize machine learning systems for search, retrieval, ranking, conversational experiences, and AI agents. You will own projects across the ML lifecycle, from problem definition and data development through experimentation, evaluation, deployment, and continuous improvement.

A key part of this role is making evaluation a first-class part of AI development. You will help define quality, build evaluation datasets and benchmarks, analyze model behavior, and use results to guide product and engineering decisions.

You will collaborate with product managers, software engineers, data scientists, research scientists, and platform teams. You will independently solve complex problems, contribute to technical direction, and mentor other engineers.

• Build and productionize machine learning models and AI systems for Search & Intelligence products.
• Own projects from concept through deployment, monitoring, evaluation, and iteration.
• Develop AI-native experiences across search, retrieval, ranking, recommendations, conversational systems, and agentic workflows.
• Design evaluation-driven development processes, including quality metrics, test cases, benchmarks, and acceptance criteria.
• Build evaluation datasets, regression suites, and pipelines for search, RAG, chat, and agentic systems.
• Conduct offline and online evaluations, human assessments, model-based evaluations, and error analysis.
• Design scalable architectures that meet requirements for quality, reliability, latency, privacy, and cost.
• Apply modern techniques in information retrieval, ranking, embeddings, NLP, deep learning, and large language models.
• Communicate technical decisions and results clearly across technical and non-technical audiences.
• Mentor engineers and contribute to engineering and ML best practices.