Senior Machine Learning Engineer, AI-Native Evaluation & Search

Atlassian•San Francisco, CA

12d•$171,063 - $269,075•Hybrid

About The Position

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. 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.

Requirements

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.

Nice To Haves

Experience building AI-native products, RAG systems, conversational assistants, or tool-using agents.
Experience with evaluation-driven development, benchmarks, regression testing, human evaluation, or LLM-as-a-judge.
Experience evaluating agent planning, tool use, task completion, or multi-step reasoning.
Experience with search relevance, ranking, recommendations, personalization, embeddings, or NLP.
Experience fine-tuning, post-training, evaluating, or optimizing large language models.
Experience with ML platforms, model serving, data pipelines, observability, or responsible AI.
Experience mentoring engineers or influencing technical direction within a team.

Responsibilities

Develop and productionize machine learning systems for search, retrieval, ranking, conversational experiences, and AI agents.
Own projects across the ML lifecycle, from problem definition and data development through experimentation, evaluation, deployment, and continuous improvement.
Make evaluation a first-class part of AI development.
Help define quality, build evaluation datasets and benchmarks, analyze model behavior, and use results to guide product and engineering decisions.
Collaborate with product managers, software engineers, data scientists, research scientists, and platform teams.
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.