Nlp / LLM Scientist - Applied AI ML Lead - Machine Learning Centre of Excellence

JPMorgan Chase JPMorgan Chase · Banking · LONDON, LONDON, United Kingdom · Corporate Sector

Research Scientist role focused on applying sophisticated machine learning methods, particularly NLP and LLMs, to real-world problems within a financial services context. The role involves developing state-of-the-art models, publishing research, and collaborating to deploy solutions into production. It emphasizes both research exploration and practical application, with a focus on post-training and potentially agentic systems.

What you'd actually do

  1. Research and explore new machine learning methods through independent study, attending industry-leading conferences, experimentation and participating in our knowledge sharing community
  2. Develop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as NLP, LLMs or recommendation systems
  3. Produce outputs that lead to high-impact business applications, open-source software, patents, and publications in top AI/ML conferences and journals.
  4. Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production
  5. Drive Firm wide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business

Skills

Required

  • Solid background in NLP and LLMs, and solid understanding of machine learning and deep learning methods
  • Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal
  • PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science with reasonable industry experience, or an MS with significant industry or research experience in the field
  • Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
  • Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
  • Experience with big data and scalable model training and solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences.
  • Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments
  • Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences. Curious, hardworking and detail-oriented, and motivated by complex analytical problem

Nice to have

  • Strong background in Mathematics and Statistics and familiarity with the financial services industries and continuous integration models and unit test development
  • Knowledge in search/ranking, Reinforcement Learning or Meta Learning
  • Expertise in recommendation systems
  • Experience with A/B experimentation and data/metric-driven product development, cloud-native deployment in a large scale distributed environment and ability to develop and debug production-quality code

What the JD emphasized

  • Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal
  • PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science with reasonable industry experience, or an MS with significant industry or research experience in the field
  • Solid background in NLP and LLMs, and solid understanding of machine learning and deep learning methods

Other signals

  • Develop state-of-the art machine learning models
  • deploy solutions into production
  • Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning