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Company Overview
A leading financial services organization is advancing trustworthy and scalable AI systems to transform the banking experience. The company leverages machine learning and AI to deliver real-time, intelligent, and automated customer solutions while maintaining high standards of reliability and ethics. Its applied science and engineering teams focus on research-driven innovation, scalable AI infrastructure, and next-generation product experiences that reshape customer interactions.
Summary
The Applied Researcher II will contribute to the development of AI foundation models and large language models (LLMs), enabling innovative AI-driven products across the organization. The role involves research, model design, training, evaluation, and deployment of AI solutions, bridging cutting-edge AI advancements with real-world applications.
Responsibilities
Partner with cross-functional teams of data scientists, software engineers, machine learning engineers, and product managers to deliver AI-powered products.
Leverage technologies including PyTorch, AWS Ultraclusters, Huggingface, Lightning, and VectorDBs to analyze large volumes of numeric and textual data.
Build AI foundation models through all phases: design, training, evaluation, validation, and implementation.
Conduct applied research to translate emerging AI developments into practical solutions.
Communicate complex technical findings in clear, actionable terms to business stakeholders.
Propose and implement novel methods for inference, representation learning, and optimization of large-scale models.
Contribute to open-source frameworks and publications, advancing the broader AI research community.
Qualifications
Basic Qualifications
PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, or in process of obtaining one by start date, with 2 years of applied research experience; or
Master’s in related field with 4 years of applied research experience.
Preferred Qualifications
PhD in Machine Learning, Computer Science, Applied Mathematics, Electrical Engineering, or related fields.
Specialization in NLP, LLM pre-training, geometric deep learning, optimization of large-scale models, or fine-tuning techniques.
Experience building large-scale deep learning models (10B+ parameters, 500B+ tokens) and deploying in real-time or streaming production environments.
Multiple publications in top-tier AI conferences (ACL, NAACL, EMNLP, NeurIPS, ICML, ICLR).
Contributions to open-source AI frameworks (e.g., PyTorch-Geometric, DGL).
Proven ability to define and execute impactful research agendas independently.
Strong engineering mindset with hands-on experience in scalable model training, inference optimization, and deployment.
Expertise in transfer learning, model guidance, instruction tuning, or dialogue fine-tuning for LLMs.
Disclaimer
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