Machine Learning Innovator

2 weeks ago

Brisbane, Queensland, Australia Bebeemachine Full time

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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Collaborate with researchers to bring new models from prototype to production and ensure they deliver meaningful value.
Build and maintain production pipelines for diffusion-based and related generative models (e.g. LoRA, ControlNet).
Optimise inference for speed, reliability, and efficiency using techniques such as quantisation, distillation, caching, and multi-GPU parallelism.
Tackle complex challenges like orchestrating multi-GPU video pipelines while ensuring systems are intuitive and maintainable.
Develop and maintain automated MLOps pipelines covering training, deployment, monitoring, and retraining.
Build CI/CD workflows for machine learning that make handovers from research to production seamless and safe.
Create scalable data pipelines and storage solutions to support high-throughput workloads.
Set up clear monitoring and alerting for model performance (e.g. Prometheus, Grafana, CloudWatch).
Design secure, reliable infrastructure on AWS (S3, EC2, SageMaker) using Infrastructure-as-Code tools like Terraform.
Be embedded in other teams, from Generations to Enterprise, to support high-impact projects requiring deep ML expertise.
Develop shared tooling, reusable workflows, and architecture patterns that help teams build and ship faster.
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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