Senior Machine Learning System Engineer (AI)

TL;DR

Senior Machine Learning System Engineer (AI): Developing and refining core infrastructure for Machine Learning models and pipelines, enabling other teams to build AI features with an accent on ML lifecycle expertise, large-scale system design, and MLOps. Focus on solving complex infrastructure and architectural issues, integrating with data platforms, and democratizing AI/ML for hirify.global’s teams.

Location: Remote globally, with options to work from offices in Sydney, Brisbane, or Melbourne, Australia. hirify.global can hire people in any country where they have a legal entity.

Company

hirify.global creates collaborative products like Jira, Confluence, and Bitbucket, aiming to unleash the potential of every team by powering revolutions across many industries.

What you will do

• Collaborate with teammates to solve complex problems from technical design to launch.
• Deliver cutting-edge solutions used by other hirify.global teams to build AI features for millions of customers.
• Deliver code reviews, documentation, and bug fixes within a strong engineering culture.
• Partner across engineering teams to take on company-wide initiatives.
• Mentor junior members of the team.

Requirements

• 5+ years of experience in building Machine Learning and AI infrastructure/platform/systems.
• Comprehensive ML lifecycle expertise: proven experience developing, deploying, and maintaining end-to-end ML systems, from data engineering to model serving and monitoring.
• Extensive experience designing and building scalable, fault-tolerant, and high-performance distributed systems for machine learning.
• Good proficiency in Python and familiarity with ML frameworks like PyTorch, TensorFlow, or JAX.
• Some experience implementing MLOps, CI/CD pipelines, and automation for continuous training, deployment, and monitoring of ML models.

Nice to have

• Hands-on expertise with major cloud platforms such as AWS, GCP, or Azure.
• Experience with distributed computing frameworks like Spark, Ray, or Dask.
• Demonstrated ability to diagnose and solve complex performance and optimization problems.
• Experience with GenAI frameworks and tools, including developing and fine-tuning LLMs and building RAG systems.

Culture & Benefits

• Choose where you work – in an office, from home, or a combination.
• A wide range of perks and benefits supporting you and your family.
• Health and wellbeing resources.
• Paid volunteer days.