Applied Researcher II – AI Foundations, LLM Core, Agentic AI

• Partner with data scientists, software engineers, machine-learning engineers, and product managers to deliver AI-powered products
• Leverage PyTorch, AWS Ultra clusters, Hugging Face, Lightning, VectorDBs, and other technologies to analyze large numeric and textual datasets
• Build AI foundation models through design, training, evaluation, validation, and implementation
• Conduct applied research to advance AI developments into customer experiences
• Translate complex research and technical work into tangible business goals
• Research and evaluate emerging technologies and apply state-of-the-art methods
• Challenge conventional thinking and collaborate with stakeholders to improve the status quo
• Support talent development for the team and broader organization
• Own and pursue a research agenda, including selecting impactful problems and carrying out long-running projects

• Currently has, or is in the process of obtaining, a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, with the required degree obtained on or before the scheduled start date, plus 2 years of experience in Applied Research
• Alternatively, an M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research
• Hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms
• Deep understanding of the foundations of AI methodologies
• Experience building large deep learning models for language, images, events, or graphs
• Expertise in one or more of training optimization, self-supervised learning, robustness, explainability, or RLHF
• Track record of delivering models at scale in training data and inference volumes
• Experience delivering libraries, platform-level code, or solution-level code to existing products
• Track record of new or improved machine-learning ideas, demonstrated through accomplishments such as first-author publications or projects
• Ability to own and pursue a research agenda, choose impactful research problems, and autonomously carry out long-running projects
• Preferred:
  PhD in a related technical field; LLM/NLP research; publications; large-language-model training; deep-learning theory; optimization; compiler design; fine-tuning; transfer learning; model deployment

Demonstrates expertise in developing AI foundation models and solutions, leveraging advanced methodologies and cloud computing platforms. Proven ability to translate complex research into actionable business goals while leading impactful research agendas.

• PhD In Electrical Engineering
• AI Foundation Model Development
• Deep Learning Model Building
• Applied Research Experience
• Training Optimization Expertise

• AI Methodologies
• Large Deep Learning Models
• Self-Supervised Learning
• Robustness
• Explainability
• Reinforcement Learning From Human Feedback
• Model Deployment
• Transfer Learning
• Fine-Tuning
• Compiler Design

• Collaboration
• Problem-Solving
• Talent Development
• Research Agenda Ownership

• Applied Research
• Machine Learning
• Natural Language Processing
• Large Language Model Training
• Deep Learning Theory

• Py Torch
• AWS Ultra clusters
• Hugging Face
• Lightning
• VectorDBs