Applied Research Scientist

HE PERSON:

This role is for you if you are passionate about reading through the latest literature, coming up with novel ideas, and implementing those through high quality code to push the boundaries on scale and performance. The ideal candidate will have both theoretical expertise and hands-on experience with developing LLMs, LMMs, and/or diffusion models. We are looking for someone who is familiar with hyper-parameter tuning methods, data preprocessing & encoding techniques and distributed training approaches for large models.

KEY RESPONSIBILITIES:

• Pre-train and finetuneover large GPU clusters while optimizing for various trade-offs.
• Improveupon the state-of-the-art inGenerative AImodel architectures and training techniques.
• Accelerate the training and inference speed acrossAMDaccelerators.
• Publish your work at top-tier conferences & workshopsand/or through technical blogs.
• Engage with academia and open-source ML communities.
• Drive continuous improvement of infrastructure and development ecosystem.

PREFERRED EXPERIENCE:

• Strong developmentand debugging skills in Python.
• Experience indeep learningframeworks(likePyTorchor TensorFlow)anddistributed training tools(likeDeepSpeedorPytorchDistributed).
• Experience with fine-tuning methods (like RLHF & DPO) as well as parameter efficient techniques (like LoRA & DoRA).
• Solid understandingofvarious types oftransformersand state space models.
• Strong publication record in top-tier conferences,workshopsorjournals.
• Solidcommunicationand problem-solving skills.

ACADEMIC CREDENTIALS:

• Advanced degree (Master'sor PhD) in machine learning, computer science, artificial intelligence, or a related field is expected. ExceptionalBachelor'sdegreecandidateswill also be considered.