Applied Research Scientist - Generative AI Models

KEY RESPONSIBILITIES:

1. Pre-train and finetune over large GPU clusters while optimizing for various trade-offs.
2. Improve upon the state-of-the-art in Generative AI model architectures and training techniques.
3. Accelerate the training and inference speed across AMD accelerators.
4. Publish your work at top-tier conferences & workshops and/or through technical blogs.
5. Engage with academia and open-source ML communities.
6. Drive continuous improvement of infrastructure and development ecosystem.

PREFERRED EXPERIENCE:

1. Strong development and debugging skills in Python.
2. Experience in deep learning frameworks (like PyTorch or TensorFlow) and distributed training tools (like DeepSpeed or Pytorch Distributed).
3. Experience with fine-tuning methods (like RLHF & DPO) as well as parameter efficient techniques (like LoRA & DoRA).
4. Solid understanding of various types of transformers and state space models.
5. Strong publication record in top-tier conferences, workshops or journals.
6. Solid communication and problem-solving skills.

ACADEMIC CREDENTIALS:

1. Advanced degree (Master’s or PhD) in machine learning, computer science, artificial intelligence, or a related field is expected. Exceptional Bachelor’s degree candidates will also be considered.