Senior Software Engineer

Senior Machine Learning System Engineer

Our organization is dedicated to driving AI innovation across all Atlassian products and platforms. We aim to deliver seamless AI experiences while establishing a robust Atlassian AI infrastructure for the future. Our purpose is to:

1. Develop horizontal AI capabilities and infrastructure that can be leveraged across all products.
2. Establish a centralized Search, Q&A, and Conversational AI system that integrates seamlessly with all Atlassian products.
3. Explore the integration of Atlassian products with AI solutions beyond the Atlassian ecosystem.

Our team's goal is to build the foundations to democratize AI and Machine Learning for Atlassian's teams, customers, and ecosystem. We aim to build productive and reliable tools that empower Atlassian teams to harness the power of AI. These tools will facilitate the development, deployment, measurement, and operation of AI & ML experiences.

Our tools are designed to integrate seamlessly with other Atlassian platforms, including the Atlassian Data Platform. This integration enables teams to efficiently and swiftly incorporate AI and ML capabilities into their workflows while strictly adhering to all security and data usage policies. Our primary goal is to deliver a smooth and hassle-free experience for Atlassian users, empowering them to harness the potential of AI and ML without any complications.

As a Senior ML System 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 culture
• Partner 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/system
• Comprehensive 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.
• 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:

• 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.
• 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.