Principal Machine Learning Engineer

Principal Machine Learning Engineer

Overview

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. Interviews and onboarding are conducted virtually, a part of being a distributed-first company.

Responsibilities

As a Principal Machine Learning Engineer, you will set the technical direction for how AI is built and shipped across Jira Service Management (JSM) - one of Atlassian's largest and most varied enterprise surfaces. You'll design and continuously improve the multi-step LLM pipelines that power our AI service experiences: shaping prompting strategies, model selection, output parsing, and orchestration to raise quality and reliability across a wide range of real-world service scenarios.
Your work will go deep on the problems that separate impressive demos from dependable enterprise products. You'll build retrieval and grounding systems that surface the right context at the right moment - knowledge articles, past requests, structured data, and tool outputs - so that AI makes decisions grounded in relevant, trustworthy information. You'll own the quality, latency, cost, and reliability of AI workflows running at enterprise scale across a large and diverse customer base, and you'll make quality measurable: designing evaluation frameworks, benchmark datasets, and scoring methods that give teams a credible, stable signal on whether AI is genuinely improving or quietly regressing.
Equally, you'll bring rigor to how we learn. You'll run disciplined experiments that validate whether changes to models, prompts, or retrieval actually move the needle - and build the infrastructure to run those experiments repeatedly and confidently. As a Principal, your influence stretches well beyond your own code: you'll set multi-quarter ML strategy, make architectural decisions that span multiple teams, mentor and grow emerging ML engineers, and raise the overall bar for how AI is built and shipped across JSM. This is a role for someone who wants their fingerprints on both the deepest technical decisions and the long-term trajectory of AI in a product used by teams worldwide.

Qualifications

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.

On the first day, we'll expect you to have

• 10+ years of total experience, with 5+ years of related industry experience in the MLE / data science domain

• Fluency in Python

• Solid understanding of machine learning concepts and algorithms, including supervised and unsupervised learning, deep learning, and NLP

• Familiarity with popular ML libraries such as scikit-learn, Keras/TensorFlow/PyTorch, numpy, and pandas

• Strong understanding of the end-to-end machine learning project lifecycle

• Experience designing and improving LLM-based pipelines — prompting strategies, model selection, output parsing, and orchestration across multi-step workflows

• Experience building retrieval and grounding systems (e.g., RAG) that surface relevant context to improve the quality and reliability of AI outputs

• Experience designing evaluation frameworks, benchmark datasets, and scoring methods that produce credible, stable quality signals

• Experience architecting and implementing high-performance RESTful microservices (API development for ML models)

• Familiarity with MLOps and experience scaling and deploying machine learning models in production, with ownership of quality, latency, cost, and reliability

• A track record of running rigorous experiments to validate impact, and building the infrastructure to do so repeatedly

• Focus on business practicality and the 80/20 rule; a very high bar for output quality, while recognizing the business benefit of "having something now" vs. "perfection sometime in the future"

• An agile development mindset, appreciating the benefit of constant iteration and improvement

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

• Deep experience with LLM-related applications and deep-learning-based models in production, at enterprise scale

• A talent for solving ambiguous and complex problems — navigating uncertainty, breaking down hard challenges into manageable components, and developing innovative solutions

• Experience setting multi-quarter ML strategy and driving architectural decisions that span multiple teams

• Experience or passion for building cutting-edge developer tools and products powered by AI/ML

Experience Level

Senior Level