Applied Researcher 4 (AI Foundations, LLM Customization, Finetuning, Reinforcement Learning)

Discover Financial
San Jose, Virginia, Cambridge, New York
Full timeFunction: Research & Scientific (R&D)Experience: 2+ yearsEducation: phdSkills: ["Interpersonal communication","Translating complexity","Innovation","Creativity","Stakeholder collaboration"]

Build and advance AI foundation models for banking, partnering with cross-functional data scientists, ML engineers, software engineers, and product managers. You’ll apply state-of-the-art techniques across design, training, evaluation, validation, and implementation—especially LLM customization, finetuning, and RLHF. Work with large-scale deep learning and production-ready systems, using tools like PyTorch, AWS Ultraclusters, Hugging Face, and Lightning, and communicate results through internal tech reports and publications.

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FursaFursa
Discover Financial
Discover Financial
14 hours ago

Applied Researcher 4 (AI Foundations, LLM Customization, Finetuning, Reinforcement Learning)

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Source: Company careers pageValidated by: Fursa AI
Last checked: 14 hours agoStatus: Live

Job Summary

Build and advance AI foundation models for banking, partnering with cross-functional data scientists, ML engineers, software engineers, and product managers. You’ll apply state-of-the-art techniques across design, training, evaluation, validation, and implementation—especially LLM customization, finetuning, and RLHF. Work with large-scale deep learning and production-ready systems, using tools like PyTorch, AWS Ultraclusters, Hugging Face, and Lightning, and communicate results through internal tech reports and publications.
Location: San Jose, Virginia, Cambridge, New York
Employment Type: Full time
Job Function: Research & Scientific (R&D)

Key Responsibilities

  • •Partner with cross-functional teams to deliver AI-powered products that improve how customers interact with their money.
  • •Leverage technologies such as PyTorch, AWS Ultraclusters, Hugging Face, and Lightning to analyze large numeric and textual datasets.
  • •Build AI foundation models across the full development lifecycle: design, training, evaluation, validation, and implementation.
  • •Conduct high-impact applied research to advance emerging AI methods into next-generation customer experiences.
  • •Collaborate with senior researchers to produce internal technical reports and/or conference submissions summarizing novel methods or findings.

Key Requirements

  • •Currently has, or is in the process of obtaining, a PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field (or an M.S. plus 2 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 AI methodology foundations and experience building large deep learning models across modalities.
  • •Experience with training optimization and techniques including self-supervised learning, robustness, explainability, and RLHF.
  • •A track record delivering models at scale across training data and inference volumes, and contributing high-quality research ideas or projects (e.g., publications).
Experience:2+ years
Education:PhD / Doctorate in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields
Skills:Interpersonal communicationTranslating complexityInnovationCreativityStakeholder collaboration
Tech Stack:PyTorchAWS UltraclustersHuggingfaceLightning

Company Brief

Discover Financial
Provides consumer banking products, credit cards, personal loans, and payment services through the Discover brand. It operates a major U.S. financial network and serves individuals and merchants with lending and digital payment solutions.
Industry: Retail Banking
Company Size: Enterprise (1,001+ employees)
Revenue: USD 10M to 25M
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: Riverwoods, United States
Founded: 1985
WebsiteLinkedIn