Machine Learning Platform Architect

1 week ago

Brisbane, Queensland, Australia beBeeEngineering Full time $180,000 - $250,000
Job Opportunity

We are seeking an experienced professional to architect foundational AI capabilities that impact how models are trained and deployed.

About the Role

This role will be responsible for leading cross-team initiatives to consolidate and improve dataset and training capabilities, working with stakeholders across multiple engineering and research teams.

Key Responsibilities
  • Architecting foundational AI platform capabilities, making key technical decisions that impact how models are trained and deployed.
  • Leading cross-team initiatives to consolidate and improve dataset and training capabilities, working with stakeholders across multiple engineering and research teams.
  • Building and maintaining high-performance distributed data processing and training systems, optimising for cost efficiency and developer experience.
  • Driving technical strategy discussions, weighing platform stability needs against product velocity requirements.
  • Mentoring other engineers and contribute to growing AI platform engineering capabilities.
  • Solving complex technical challenges spanning multiple parts of the ML stack.
About You

To be successful in this role, you will need to have over 5 years of experience building and scaling ML training systems, with hands-on experience in distributed training, model lifecycle management, and large-scale data processing. You will also need to have a proven track record of designing and implementing foundational AI/ML infrastructure that supports multiple teams and use cases.

What We Offer

At our organisation, we empower thousands of engineers and product managers to deliver amazing product features which harness the power of cutting-edge technologies. Our GenAI Platform Group is responsible for delivering Capabilities and Solutions which support ML and AI initiatives, from early ideation and prototyping, through to scaling to meet the needs of millions of users in production.

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