Senior Machine Learning Engineer, AI-Native Evaluation & Search

Overview 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.

Responsibilities 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.

What You’ll Do

• 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.

Compensation At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate’s skills, knowledge, and experience when setting base pay within the range.

Please visit go.atlassian.com/payzones for more information on which locations are included in each of our geographic pay zones. However, please confirm the zone for your specific location with your recruiter.

This role may also be eligible for benefits, bonuses, commissions, and equity.

Pay Ranges Role In The United States , we have three geographic pay zones. For this role, our current base pay ranges for new hires in each zone are:

Zone A $206,100 - $269,075 Zone B $185,490 - $242,168 Zone C $171,063 - $223,332

Qualifications On your first day, we’ll expect you to have

• A Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field, or equivalent experience.
• At least 4 years of experience developing and deploying machine learning or AI systems in production.
• Strong Python skills and experience with Java, Kotlin, TypeScript, or another production language.
• Experience with SQL and large-scale data processing technologies such as Spark.
• Experience building, evaluating, deploying, and scaling models with large datasets.
• Experience designing evaluation strategies, analyzing model quality, and using results to guide improvements.
• Familiarity with cloud-based ML and data platforms such as AWS or Databricks.
• Ability to independently solve ambiguous problems and deliver practical, production-quality solutions.
• Strong communication and collaboration skills.
• An agile mindset and commitment to continuous improvement.

It’s Great, But Not Required, If You Have

• Experience building AI-native products, RAG systems, conversational assistants, or tool-using agents.
• Experience with evaluation-…