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2 weeks ago
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 OpportunityWe 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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