Artificial Intelligence Researcher

THE ROLE:The AI Models team is looking for exceptional machine learning scientists and engineers to explore and innovate on training and inference techniques for large language models (LLMs), large multimodal models (LMMs), image/video generation and other foundation models as well as self-evolving agents on top of these. 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: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 and optimizing LLMs, LMMs, and/or diffusion models.KEY RESPONSIBILITIES:Improve upon the state-of-the-art in Generative AI model architectures and their compatibility on AMD acceleratorsAccelerate the training and inference speed through various optimizations.Build AI agents to automatically write and evaluate efficient kernels and codeDrive continuous improvement of infrastructure and development ecosystemPublish your research at top-tier conferences, workshops and/or through technical blogs.Engage with academia and open-source ML communities.PREFERRED EXPERIENCE:Strong development and debugging skills in Python.Experience in deep learning frameworks (like PyTorch or TensorFlow), distributed training tools as well as various agentic AI frameworksSolid understanding of various types of transformers and state space models.Experience in writing kernels using HIP, CUDA, Triton, etc.prior experience in building self-evolving AI agents for various challenging tasks (like code generation, discovery, etc.)knowledge of latest research in the field of AI agentsfamiliar with techniques to do hardware-efficient training and inference of large language models and also familiarity with existing frameworks (e.g. vllm, sglang, etc.)expertise in model architecture (beyond just transformers only models) and latest research in this fieldhaving relevant open-source contributionsStrong publication record in top-tier conferences, workshops or journals.Solid communication and problem-solving skills.Passionate about learning new stuffs in this domain as well as innovating on top of itACADEMIC CREDENTIALS:Advanced degree (Master’s or PhD) in machine learning, computer science, artificial intelligence, or a related field is expected. Exceptional Bachelor’s degree candidates with years of relevant research experience may also be considered.