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Principal Machine Learning Systems Engineer - Search & Intelligence

Job in Mountain View, Santa Clara County, California, 94040, USA
Listing for: Atlassian
Full Time position
Listed on 2026-10-25
Job specializations:
  • Software Development
    Machine Learning/ ML Engineer, AI Engineer (Applied/Software), Software Engineer
Salary/Wage Range or Industry Benchmark: 236700 - 309025 USD Yearly USD 236700.00 309025.00 YEAR
Job Description & How to Apply Below
Overview

Atlassian is a global leader in team collaboration and productivity software, empowering teams to unleash their potential and achieve remarkable results. Our suite of tools, including Jira, Confluence, Trello, Bitbucket, and Rovo, is trusted by millions of users worldwide to plan, track, and manage their work effectively. At Atlassian, we are dedicated to building innovative tools and technologies that drive the future of work.

We are looking for a Principal Machine Learning Engineer to join our Search & Intelligence organization. The team builds Atlassian’s flagship AI products, including agentic search and chat, long-horizon agents, and capabilities that enrich Atlassian’s Teamwork Graph. We also develop the frameworks, models, and infrastructure that accelerate AI feature development across the company.

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

What you’ll doAs a Principal Machine Learning Engineer, you will lead the development and implementation of cutting-edge machine learning algorithms and sophisticated models that power AI capabilities across Atlassian’s products and services. You will collaborate closely with product, engineering, and analytics teams to translate complex business and customer needs into impactful solutions.

Your responsibilities will span designing system and model architectures, leading rigorous experimentation and evaluation, and providing technical mentorship to emerging ML engineers. Beyond these core responsibilities, you will help shape our AI strategy and ensure its transformative potential is realized across Atlassian.

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  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

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: $236,700 - $309,025

Zone B: $213,030 - $278,123

Zone C: $196,461 - $256,491

Qualifications

On your first day, we’ll expect you to haveA bachelor’s or master’s degree in computer science or a related field, or equivalent practical experience.

At least five years of relevant industry experience in machine learning, data science, or a related discipline.

Strong programming skills in Python and one or more production languages such as Java, Kotlin, or Type Script.

Proficiency in SQL and experience with Spark and cloud-based data environments such as AWS or Databricks.

Experience building, deploying, and scaling machine learning models for business applications using large, complex datasets.

Strong communication skills, including the ability to explain data science concepts to diverse audiences and tell compelling, data-driven stories.

A pragmatic, business-focused approach. You hold a high bar for quality while applying the 80/20 principle and recognizing when delivering a valuable solution today is better than pursuing perfection later.

An agile mindset and an appreciation for continuous iteration, learning, and improvement.

It’s great, but not required, if you have Experience fine-tuning, post-training, and optimizing…
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