Machine Learning Platform Engineer

Posted 13 Hours Ago
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Hiring Remotely in Sydney, New South Wales, AUS
In-Office or Remote
Junior
Cloud • Information Technology • Productivity • Security • Software • App development • Automation
Atlassian provides tools to help every team unleash their full potential.
The Role
Build and maintain ML platform infrastructure to enable model creation, training, deployment, and monitoring. Collaborate with product teams to curate datasets, fine-tune LLMs, implement MLOps/CI-CD pipelines, optimize performance, and deliver scalable, fault-tolerant ML systems used across Atlassian products.
Summary Generated by Built In
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.
As an 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
  • 2+ 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.
  • MLOps and automation: Deep experience implementing MLOps, CI/CD pipelines, and automation for continuous training, deployment, and monitoring of ML models.

At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate's skills, knowledge, and experience when setting base pay within the range.
This role may also be eligible for benefits, bonuses, commissions, and equity.
Qualifications
  • 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.
  • 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.

Benefits & Perks
Atlassian offers a wide range of perks and benefits designed to support you, your family and to help you engage with your local community. Our offerings include health and wellbeing resources, paid volunteer days, and so much more. To learn more, visit go.atlassian.com/perksandbenefits .
About Atlassian
At Atlassian, we're motivated by a common goal: to unleash the potential of every team. Our software products help teams all over the planet and our solutions are designed for all types of work. Team collaboration through our tools makes what may be impossible alone, possible together.
We believe that the unique contributions of all Atlassians create our success. To ensure that our products and culture continue to incorporate everyone's perspectives and experience, we never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status. All your information will be kept confidential according to EEO guidelines.
To provide you the best experience, we can support with accommodations or adjustments at any stage of the recruitment process. Simply inform our Recruitment team during your conversation with them.
To learn more about our culture and hiring process, visit go.atlassian.com/crh .
In line with local law, identity verification (which may include use of biometric data) is a condition of employment with Atlassian for employment fraud purposes.

Skills Required

  • 2+ years building machine learning and AI infrastructure/platforms/systems
  • End-to-end ML lifecycle expertise (data engineering, model serving, monitoring)
  • Implementing MLOps, CI/CD pipelines, and automation for continuous training and deployment
  • Designing and building scalable, fault-tolerant distributed systems for ML
  • Expert-level proficiency in Python
  • Expert-level experience with ML frameworks such as PyTorch, TensorFlow, or JAX
  • Familiarity with additional languages like Go, Java, or Scala
  • Hands-on experience with cloud platforms (AWS, GCP, or Azure) and GPU compute
  • Experience with distributed data processing frameworks (Spark, Ray, or Dask)
  • Performance optimization for ML models and infrastructure
  • Experience with Generative AI, fine-tuning LLMs, and building retrieval-augmented generation (RAG) systems

What the Team is Saying

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Atlassian Compensation & Benefits Highlights

  • Parental & Family Support Generous paid parental leave (26 weeks for birthing parents and 20 weeks for non‑birthing parents) is paired with inclusive family‑formation benefits such as fertility, adoption, and surrogacy support. Caregiving resources, onsite Mother’s Rooms in some locations, and breastmilk shipping for business travel reinforce a family‑friendly approach.
  • Retirement Support A company 401(k) match with immediate vesting, along with retirement planning support and broader financial wellbeing resources, strengthens long‑term security. These offerings complement market‑based cash compensation to round out total rewards.
  • Flexible Benefits The “Team Anywhere” model is backed by a monthly remote‑work allowance, ergonomic resources, and a Flex Wallet that can be applied to a wide range of reimbursable expenses. Learning budgets and paid volunteer time further expand choice and lifestyle flexibility.

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The Company
HQ: San Francisco, CA
11,000 Employees
Year Founded: 2012

What We Do

Atlassian creates teamwork solutions for high-performing teams. Our portfolio of collaboration and work management software products includes Jira, Confluence, Trello, Loom and Rovo. More than 300,000 businesses worldwide rely on Atlassian’s technology, including 80 percent of Fortune 500 companies. Our solutions support various business teams and they help organizations plan, track, and deliver their biggest ideas together.

Why Work With Us

At Atlassian, we believe we can accomplish so much more together than apart — which is why everything from our tooling — to our distributed workforce — to how our teams are structured is rooted in collaboration. Come join us and help unleash the potential of every team.

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Atlassians have flexibility in where they work to support their family, personal goals, and other priorities. Our approach to distributed work allows us to tap into talent beyond our office locations, and to reimagine how work gets done.

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