Machine Learning Innovator

1 day ago

Brisbane, Queensland, Australia beBeeMachine Full time $130,000 - $170,000

Job Description

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  • We are seeking a highly skilled professional to fill the position of Senior Machine Learning Engineer. The ideal candidate will have extensive experience in deploying diffusion-based generative models into production and working with inference optimisation techniques.
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About the Role

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  • This role requires a strong technical background, with proficiency in building automated, reliable workflows for training, deploying, and monitoring machine learning models at scale.
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Your Responsibilities

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  1. Collaborate with researchers to bring new models from prototype to production and ensure they deliver meaningful value.

  2. Build and maintain production pipelines for diffusion-based and related generative models (e.g. LoRA, ControlNet).

  3. Optimise inference for speed, reliability, and efficiency using techniques such as quantisation, distillation, caching, and multi-GPU parallelism.

  4. Tackle complex challenges like orchestrating multi-GPU video pipelines while ensuring systems are intuitive and maintainable.

  5. Develop and maintain automated MLOps pipelines covering training, deployment, monitoring, and retraining.

  6. Build CI/CD workflows for machine learning that make handovers from research to production seamless and safe.

  7. Create scalable data pipelines and storage solutions to support high-throughput workloads.

  8. Set up clear monitoring and alerting for model performance (e.g. Prometheus, Grafana, CloudWatch).

  9. Design secure, reliable infrastructure on AWS (S3, EC2, SageMaker) using Infrastructure-as-Code tools like Terraform.

  10. Be embedded in other teams, from Generations to Enterprise, to support high-impact projects requiring deep ML expertise.

  11. Develop shared tooling, reusable workflows, and architecture patterns that help teams build and ship faster.

  12. Promote knowledge-sharing, best practices, and scalable solutions across the organisation.

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What We're Looking For

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  • Generative AI Experience: Experience deploying diffusion-based or similar generative models into production and working with inference optimisation techniques.

  • MLOps Expertise: Skilled in building automated, reliable workflows for training, deploying, and monitoring ML models at scale.

  • Infrastructure & Cloud: Hands-on with AWS (S3, EC2, SageMaker), Kubernetes, Docker, and Infrastructure-as-Code (Terraform).

  • Performance & Efficiency: Proficient in techniques such as quantisation, distillation, caching, and distributed inference.

  • Data Foundations: Ability to design scalable data pipelines and storage solutions (SQL/NoSQL).

  • Engineering Craft: Strong Python skills and a focus on writing clear, maintainable, and collaborative code.

  • Collaboration & Growth: Thrive in cross-functional teams, value open feedback, and enjoy supporting others' success while learning continuously.

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