Senior Principal Machine Learning Engineer - Central AI

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 Senior Principle Machine Learning Engineer to join us on our journey as we redefine productivity and collaboration in the AI age. As a Senior Principle Machine Learning Engineer you will be the driving force in integrating cutting-edge AI capabilities within Atlassian products. Your work will transform the way teams interact with our products as we look to extract knowledge and insights from data to propel teamwork forward.

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

What you’ll do

As a Senior Principal Machine Learning Engineer, your role will be instrumental in steering the development and deployment of advanced machine learning algorithms and system architecture. You will be tasked with training complex models and working in close collaboration with product, backend/front end engineering, and analytics teams to seamlessly integrate AI functionalities into every Atlassian product and service. Your day-to-day responsibilities will span a wide range of tasks, including designing system and model architectures, conducting meticulous experimentation and model evaluations, and mentoring budding ML engineers. Your role extends beyond these tasks, playing a crucial part in harnessing the transformative power of AI across our product suite.

Your role and impact:

• AI-Powered Atlassian Integration: Craft and implement innovative machine learning models in Atlassian tools.

• Advanced Data Analysis: Dive deep into vast datasets to extract meaningful insights. Your work will power the AI models to understand complex user interactions and content within Atlassian tools to propel team work forward across the globe.

• Robust Model Development: Design, build, and refine models that can accurately perform tasks such as topic and entity extraction, search ranking, and retrieval augmented generation at scale.

• Collaborative Innovation: Join forces with product managers, designers, and developers. Your collaborative efforts will ensure that AI features are not only powerful but also intuitive and user-friendly.

• AI Evangelism: Be the voice of AI within the team. Educate and inspire your colleagues about the potential of AI, leading workshops and knowledge-sharing sessions.

Qualifications

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

• Ph.D. or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.

• 8+ years of experience in machine learning, with a focus on large-scale model development and optimization.

• Deep expertise in LLM and transformer architectures (e.g., GPT, BERT, T5).

• Strong proficiency in Python and ML frameworks such as PyTorch, JAX, or TensorFlow.

• Experience with distributed training techniques and large-scale data processing pipelines.

• Proven track record of deploying machine learning models in production environments.

• Familiarity with model optimization techniques, including quantization, pruning, and knowledge distillation.

• Strong problem-solving skills and ability to work in a fast-paced, collaborative environment.

• Excellent communication skills and ability to translate technical concepts for diverse audiences.

It's great, but not required, if you have

• Experience with LLMs and domain-specific fine-tuning.

• Knowledge of cloud-based ML platforms (e.g., AWS, GCP, Azure).

• Experience working in a consumer or B2C space for a SaaS product provider, or the enterprise/B2B space

• Experience in developing deep learning-based models and working on LLM-related applications

• Contributions to open-source ML projects or publications in top-tier conferences.

• Familiarity with MLOps practices and tools.