Senior ML Platform Engineer

UNAVAILABLE; Sydney; Hybrid; AUD 180,000 - 260,000; Full time

Job summary

At Atlassian, a Senior Machine Learning System Engineer will join the AI & ML Platform team to design and scale core ML infrastructure across Atlassian products. You will empower software engineers, ML engineers and data scientists to build, train, evaluate, deploy, and manage ML models and pipelines.

You will collaborate with Jira/Confluence teams, curate data, fine-tune LLMs, and tackle challenging ML problems, while contributing to robust, secure, and scalable AI tooling and workflows.

Qualifications

• 6+ years of general software engineering experience with 2+ years in building Machine Learning and AI infra/Platform/Systems.
• Comprehensive ML lifecycle expertise: develop, deploy, and maintain end-to-end ML systems.
• Large-scale system design: scalable, fault-tolerant, high-performance distributed systems for ML.
• Proficiency with Python and ML frameworks like PyTorch, TensorFlow, or JAX.
• MLOps and automation: CI/CD pipelines and automation for continuous training, deployment, and monitoring of ML models.

Responsibilities

• Collaborate with product teams to solve complex ML problems and build ML solutions.
• Curate high-quality ML datasets, fine-tune open-source LLMs or access proprietary LLMs.
• Lead projects from technical design through launch.
• Deliver code reviews, documentation, and bug fixes within a strong engineering culture.
• Mentor junior team members and partner across engineering teams on company-wide initiatives.

Skills

ML infra

Distributed systems

Python

Tools

PyTorch

TensorFlow

JAX

Job description

At Atlassian, a Senior Machine Learning System Engineer will join the AI & ML Platform team to design and scale core ML infrastructure across Atlassian products. You will empower software engineers, ML engineers and data scientists to build, train, evaluate, deploy, and manage ML models and pipelines.

You will collaborate with Jira/Confluence teams, curate data, fine-tune LLMs, and tackle challenging ML problems, while contributing to robust, secure, and scalable AI tooling and workflows.