Innovative AI Developer

2 weeks ago

Sydney, New South Wales, Australia beBeeArtificial Full time $180,000 - $250,000
AI Innovation Role

EML is a leading provider of workers compensation and personal injury claims management services. Our goal is to help people regain control over their lives through ongoing support during their return-to-work journey.

The Opportunity

We are at the forefront of the AI revolution, building innovative products that leverage the latest advancements in generative AI and large language models to solve real-world problems.

Responsibilities
  • Design, train, and optimize AI models specialized in audio tasks, computer vision, and NLP tasks.
  • Research and implement cutting-edge algorithms and techniques from the latest publications in the field.
  • Optimize model performance and efficiency for inference and deployment in production environments.
  • Collaborate with data engineers and MLOps engineers to build and maintain robust data pipelines for model training and evaluation.
  • Conduct thorough testing and validation of models to ensure accuracy and stability.
  • Work with MLOps/Cloud Engineer and contribute to the development of our machine learning infrastructure and best practices.

This is a hands-on role for someone who is equally passionate about the theoretical foundations of deep learning and the engineering challenges of deploying efficient, reliable models at scale.

About You
  • Master's or PhD degree in Computer Science, or a related technical field.
  • 3+ years of professional experience in ML engineering, or software development.
  • Proven track record of productionising ML models and deploying scalable AI solutions on cloud platforms.
  • Strong foundational knowledge in NLP, Computer Vision, or Audio processing.
  • Deep understanding of transformer architectures, attention mechanisms, and autoregressive models.
  • Hands on experience with the full lifecycle of LLM fine-tuning including Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), and parameter-efficient methods (LoRA, QLoRA).
  • Practical experience with model optimization techniques, such as Quantization (GPTQ, AWQ, bitsandbytes), pruning, or knowledge distillation.

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