Senior Machine Learning Engineer, Search & Intelligence

Atlassian•Mountain View, CA

20h•$171,063 - $269,075•Hybrid

About The Position

Atlassian is looking for a Senior Machine Learning Engineer to join our Search & Intelligence organization. Our team builds the intelligent experiences, agentic systems, models, evaluation frameworks, and data pipelines that power Atlassian’s AI products and accelerate AI innovation across the company. 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, Artificial Intelligence, Data Science, or a related field, or equivalent practical experience.
At least 5 years of professional experience developing and deploying machine learning or data science solutions in production.
Strong programming skills in Python and one or more production languages such as Java, Kotlin, or TypeScript.
Strong knowledge of SQL and experience working with large-scale data processing technologies such as Spark.
Experience designing, training, evaluating, deploying, and scaling machine learning models using large and complex datasets.
Experience building performant, reliable, and maintainable production-quality software.
Familiarity with cloud-based data and machine learning environments, such as AWS, Databricks, or comparable platforms.
Experience designing evaluation strategies and using metrics, experimentation, and error analysis to guide model and product improvements.
Experience collaborating effectively across product, engineering, analytics, and data science teams.
An ability to independently navigate ambiguous and complex problems, break them into manageable components, and deliver practical solutions.
Strong written and verbal communication skills, with the ability to explain complex technical concepts clearly.
An agile development mindset and an appreciation for rapid iteration, continuous improvement, and learning from results.

Nice To Haves

End-to-end experience integrating machine learning or AI capabilities into customer-facing products.
Experience building search, recommendation, ranking, personalization, or natural language processing systems.
Experience developing deep learning models and applying large language models to production use cases.
Experience with agentic systems, tool-using models, or multi-step reasoning and planning systems.
Experience fine-tuning, evaluating, monitoring, and optimizing large language models.
Experience working in a consumer or B2C environment, a SaaS product organization, or an enterprise B2B environment.
Experience with ML platforms, model serving, feature stores, data pipelines, observability, or responsible AI practices.
A track record of technical leadership, influencing architecture and strategy beyond your immediate team, and mentoring other engineers.
Experience balancing long-term technical investments with pragmatic delivery in an evolving product environment.

Responsibilities

Lead the design, development, and implementation of state-of-the-art machine learning algorithms and models for production environments.
Own machine learning projects from initial concept through production deployment, measurement, and continuous improvement.
Develop scalable data and modeling approaches using large, complex datasets.
Design robust system and model architectures that meet requirements for quality, latency, scale, reliability, privacy, and cost.
Build and improve machine learning solutions across areas such as information retrieval, search ranking, personalization, natural language processing, deep learning, and large language model applications.
Design and execute rigorous experiments, offline evaluations, online tests, and error analyses to measure model quality and product impact.
Collaborate with product, engineering, data science, analytics, and platform teams to integrate AI capabilities into Atlassian products and services.
Translate research and emerging AI techniques into reliable, maintainable, production-quality systems.
Identify opportunities to improve model performance, operational efficiency, developer experience, and customer outcomes.
Communicate technical decisions, trade-offs, results, and recommendations clearly to both technical and non-technical audiences.
Mentor and support machine learning engineers, contribute to technical strategy, and help establish best practices across the organization.
Contribute to a culture of experimentation, continuous learning, inclusive collaboration, and iterative delivery.