AI Infrastructure Architect

14 hours ago


Brisbane, Queensland, Australia beBeeMachineLearning Full time $180,000 - $280,000

Job Description

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As a Staff Machine Learning Engineer, you will play a key role in architecting foundational AI platform capabilities and leading cross-team initiatives to improve dataset and training capabilities. The ideal candidate has 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.

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Key Responsibilities:

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  • 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.">
  • Building and maintaining paved roads for high-performance distributed data processing and training systems.">
  • Driving technical strategy discussions, weighing platform stability needs against product velocity requirements.">
  • Mentoring other engineers and contributing to growing AI platform engineering capabilities.">
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Required Skills and Qualifications:

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You have a proven track record of designing and implementing foundational AI/ML infrastructure that supports multiple teams and use cases. You possess a strong understanding of distributed computing, container orchestration (Kubernetes), and cloud infrastructure. Proficiency in Python with deep knowledge of ML frameworks (PyTorch, TensorFlow) and modern tools (W&B, Ray, Anyscale) is essential. Experience working with generative AI systems, including LLMs, multimodal models, and foundation model fine-tuning, is highly valued.

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Benefits:

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As a Staff Machine Learning Engineer, you will be part of a dynamic team working on cutting-edge AI projects. You will have the opportunity to grow your skills and contribute to the development of AI platform engineering capabilities. A strong understanding of performance optimisation and experience with infrastructure-as-code are also desirable.

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Others:

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Consideration will be given to candidates who can balance long-term platform investments with immediate business needs, making pragmatic technical decisions. Experience working with product teams, research, and other engineering specialties is also advantageous.



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