Advanced AI Research Engineer Position

2 days ago

Dublin, Dublin City, Ireland beBeeEfficiency Full time €120,000 - €140,000
AI Research Engineer - Model Inference

We are seeking a highly skilled AI Model Engineer with expertise in kernel development, model optimization, fine-tuning, and GPU acceleration. The engineer will extend the inference framework to support inference and fine-tuning for language models with a strong focus on mobile and integrated GPU acceleration (Vulkan).

This role requires hands-on experience with quantization techniques, LoRA architectures, Vulkan backend, and mobile GPU debugging.

  • Develop and optimize custom inference and fine-tuning kernels for small and large language models across multiple hardware backends.
  • Implement and optimize full and LoRA fine-tuning for small and large language models across multiple hardware backends.
  • Design and extend datatype and precision support (int, float, mixed precision, ternary QTypes, etc.).
  • Design, customize, and optimize Vulkan compute shaders for quantized operators and fine-tuning workflows.
  • Investigate and resolve GPU acceleration issues on Vulkan and integrated/mobile GPUs.
  • Architect and prepare support for advanced quantization techniques to improve efficiency and memory usage.
  • Debug and optimize GPU operators (e.g., int8, fp16, fp4, ternary).
  • Integrate and validate quantization workflows for training and inference.
  • Conduct evaluation and benchmarking (e.g., perplexity testing, fine-tuned adapter performance).
  • Conduct GPU testing across desktop and mobile devices.
  • Collaborate with research and engineering teams to prototype, benchmark, and scale new model optimization methods.
  • Deliver production-grade, efficient language model deployment for mobile and edge use cases.
  • Work closely with cross-functional teams to integrate optimized serving and inference frameworks into production pipelines designed for edge and on-device applications.
Key Responsibilities:
  • Improve real-world performance of language models.
  • Ensure low error rates in model predictions.
  • Guarantee robust scalability and optimal memory usage.

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