Applied Research Scientist

3 days ago

Bengaluru Karnataka, India Advanced Micro Devices Full time

**Applied Research Scientist - Generative AI Models**:

- Bangalore, India
- Engineering
- 47051

**Job Description**:
**WHAT YOU DO AT AMD CHANGES EVERYTHING**
- We care deeply about transforming lives with AMD technology to enrich our industry, our communities, and the world. Our mission is to build great products that accelerate next-generation computing experiences - the building blocks for the data center, artificial intelligence, PCs, gaming and embedded. Underpinning our mission is the AMD culture. We push the limits of innovation to solve the world’s most important challenges. We strive for execution excellence while being direct, humble, collaborative, and inclusive of diverse perspectives.
- AMD together we advance_

**THE ROLE**:

- The AI Models and Applications team is looking for an exceptional machine learning scientist / engineer to explore and innovate on architectures and training techniques for large language models (LLMs), large multimodal models (LMMs), image/video generation and other foundation models. You will be part of a world-class research and development team focussing on efficient and scalable pre-training, instruction tuning, alignment and optimization. As an early member of the team, you can help us shape the direction and strategy to fulfill this important charter.

**THE PERSON**:
**KEY RESPONSIBILITIES**:

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

**PREFERRED EXPERIENCE**:

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

**ACADEMIC CREDENTIALS**:

- #LI-MK1

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