Principal Engineer

Principal EngineerAs a Principal Engineer on the AI & ML Platform team, you will play a pivotal role in developing and refining the core infrastructure that empowers all Atlassian software engineers, ML engineers, and data scientists to create, train, evaluate, deploy, and manage Machine Learning models and pipelines.You will collaborate closely with product teams, such as Jira and Confluence, to solve their specific challenges in building ML solutions. This may involve curating high-quality ML datasets, fine-tuning open-sourced Large Language Models (LLMs), or accessing proprietary LLMs. Your expertise in both ML and software development expertise will be instrumental in overcoming challenging problems and navigating complex infrastructure and architectural issues.This position offers you the chance to lead projects from the technical design phase all the way to launch. You will partner with various teams and internal stakeholders to achieve impactful results.In this role, you'll get the chance to:Collaborate with your teammates to solve complex problems, from technical design to launch.Deliver cutting-edge solutions that are used by other Atlassian teams and products to build AI features that reach millions of customers.Deliver code reviews, documentation & bug fixes within a strong engineering culturePartner across engineering teams to take on company-wide initiatives spanning multiple projects.Mentor junior members of the team.On your first day, we'll expect you to have:5+ years of experience in building Machine Learning and AI infra/platform/systemComprehensive ML lifecycle expertise : proven experience developing, deploying, and maintaining end-to-end ML systems, from data engineering to model serving and monitoring.Large-scale system design: Extensive experience designing and building scalable, fault-tolerant, and high-performance distributed systems for machine learning.Proficiency with frameworks and languages: Expert-level proficiency in Python and ML frameworks like PyTorch, TensorFlow, or JAX. Familiarity with other languages like Go, Java, or Scala is also beneficial.MLOps and automation: Deep experience implementing MLOps, CI/CD pipelines, and automation for continuous training, deployment, and monitoring of ML models.It would be great, but not required if you have:Cloud infrastructure: Hands-on expertise with major cloud platforms such as AWS, GCP, or Azure, including their specific AI/ML services and compute resources like GPUs.Big data processing: Experience with distributed computing frameworks for large-scale data processing, such as Spark, Ray, or Dask.Performance optimization: A demonstrated ability to diagnose and solve complex performance and optimization problems for ML models and infrastructure.Generative AI systems: Experience with GenAI frameworks and tools, including developing and fine-tuning large language models (LLMs) and building retrieval-augmented generation (RAG) systems.