Deep Learning ML Researcher

• Proficiency in creating and using algorithms to meticulously investigate and work through large data or error-checking problems

• Deep knowledge of LLM architectures, including transformers.

• Familiarity with attention mechanisms, normalization techniques, and model architecture design.

• Training techniques (pre-training, fine-tuning, RLHF), and optimization methods.

• Understanding of low-level details like GPU memory management, precision types (float16, bfloat16), and parallelization techniques.

• Proficiency in advanced training techniques such as pre-training, fine-tuning, RLHF, and DPO.

• Expertise in Python and ML frameworks like PyTorch or TensorFlow.

• Familiarity with Retrieval Augmented Generation (RAG) systems and their implementation.

• Proven ability to approach open-ended problems and design end-to-end solutions in ML/AI.

• Strong mathematical and statistical foundations, particularly in areas relevant to quantitative finance.