Principal Machine Learning Engineer

About Atlassian
Atlassian's mission "to unleash the potential of every team" is the guiding light behind what we do. We have developed well-known products such as Jira, Confluence, and Bitbucket, that fit into the fabric of teamwork across different types of teams and the processes to help every team succeed.
Atlassian helps teams everywhere change the world. Our products are revolutionizing the software industry, and helping teams collaborate and create the magic that provides their best work. Think NASA launching the Rover on Mars or Cochlear gifting those born deaf with the ability to hear, your work directly impacts the products they use to promote humanity.
About ITSM Team
IT Service Management (ITSM) is one of the fastest growing products of Atlassian. Through this solution, we are helping technical and non-technical teams centralize and streamline service requests, respond to incidents, collect and maintain knowledge, manage assets and configuration items, and more. Specifically this team within ITSM works on using cutting-edge AI and machine learning algorithms to automate IT operational tasks, troubleshoot problems, and reduce mental overload for oncall engineers and alike. By weaving Generative AI into our product we revolutionise AIOps by moving from a traditional reactive troubleshooting-based system to a proactive problem-solving approach. Our AI models are designed to evolve over time through continuous learning making them more robust, accurate, and resilient.
What you will do
As a Principal Engineer on the ITSM team, you will get the opportunity to work on cutting-edge AI and ML algorithms that help modernize IT Operations by reducing MTTR (mean time to resolve), and MTTI ( Mean time to identify). You will use your software development expertise to solve difficult problems, tackling complex infrastructure and architecture challenges.
You will have the opportunity to lead engineers to drive involved projects from technical design to launch. You will also collaborate with other teams and internal customers to set expectations, gather input, and communicate results.
In this role, you'll get the chance to:

• Shape the future of AIOps: Be at the forefront of innovation, shaping the next generation of AI-powered operations tools that predict, prevent, and resolve IT issues before they impact our customers
• Master Generative AI: Delve into the world of generative models, exploring their potential to detect anomalies, automate responses, and personalize remediation plans
• Become a machine learning maestro: Hone your skills in both supervised and unsupervised learning, constructing algorithms that analyze mountains of data to uncover hidden patterns and optimize system performance
• Collaborate with diverse minds: Partner with a brilliant team of engineers, data scientists, and researchers, cross-pollinating ideas and learning from each other's expertise
• Make a tangible impact: Your work will directly influence the reliability and performance of Atlassian's critical software, driving customer satisfaction and propelling our business forward. This is an extremely high-visibility role
• Routinely tackle complex architectural challenges, spar with other principal engineers to build ML pipelines and models that scale for thousands of customers
• Lead code reviews & documentation as well as take on complex bug fixes, especially on high-risk problems.
• Develop leadership skills: You'll have the opportunity to mentor junior engineers, guide projects, and partner across engineering teams to take on company-wide initiatives spanning multiple projects.
• Our tech stack is primarily Python/Java/Kotlin built on AWS.

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

• 10+ years of total experience
• 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 like sci-kit-learn, Keras/TensorFlow/PyTorch, numpy, pandas
• Good Understanding of Machine Learning project lifecycle
• Experience in architecting and implementing high-performance RESTful microservices ( API development for ML Models )
• Familiarity with MLOps and experience with scaling and deploying Machine Learning models

It would be great, but not required if you have

• Experience with cloud-based machine learning platforms (e.g., AWS SageMaker, Azure ML Service, Databricks).
• Experience with MLOps tools ( MLflow, Tecton, Pinecone, Feature Stores)
• Experience with AIOps or related fields like IT automation or incident management.
• Experience building and operating large-scale distributed systems using Amazon Web Services (S3, Kinesis, Cloud Formation, EKS, AWS Security and Networking).
• Experience with using OpenAI LLMs.