Applied Researcher 4 (AI Foundations, LLM Core and Agentic AI)

About the job

Responsibilities

• Partner with data scientists, software engineers, ML engineers, and product managers to deliver AI-powered products.
• Utilize technologies like PyTorch, AWS Ultraclusters, Huggingface, and Lightning to analyze large datasets.
• Build AI foundation models from design through training, evaluation, validation, and implementation.
• Conduct applied research to advance customer experiences with the latest AI developments.
• Translate complex AI work into tangible business goals.
• Collaborate with senior researchers to prepare internal tech reports or conference submissions.

Requirements

• PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, or an MS with 2 years of applied research experience.
• Experience building large deep learning models (language, images, events, or graphs).
• Expertise in training optimization, self-supervised learning, robustness, explainability, or RLHF.
• Engineering mindset with a track record of delivering models at scale for training data and inference volumes.
• Experience delivering libraries, platform-level code, or solution-level code to existing products.
• Track record of high-quality ideas or improvements in machine learning, demonstrated by first author publications or projects.
• Ability to own and pursue a research agenda, including choosing impactful problems and autonomously carrying out long-running projects.
• Deep understanding of AI methodologies.
• Comfort with open-source languages and developing AI foundation models and solutions using open-source tools and cloud platforms.
• PhD focus on NLP or Masters with 5 years of industrial NLP research experience.
• Multiple publications on pre-training of large language models.
• Experience training a large language model from scratch (10B+ parameters, 500B+ tokens).
• Publications in deep learning theory or at major AI conferences (ACL, NAECM, EMNLP, Neurips, ICML, ICLR).
• Experience with optimization techniques for training and inference, including model sparsification, quantization, training parallelism, gradient checkpointing, and model compression.
• Experience optimizing training for a 10B+ model.
• Deep knowledge of deep learning algorithmic and/or optimizer design.
• Experience with compiler design.
• Experience with fine-tuning LLMs (Supervised Finetuning, Instruction-Tuning, Dialogue-Finetuning, Parameter Tuning).
• Demonstrated knowledge of transfer learning, model adaptation, and model guidance principles.
• Experience deploying a fine-tuned large language model.