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

JPMorgan Chase & Co.
London, United Kingdom
5 months ago
Applications closed

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  • Research and explore new machine learning methods through independent study, attending industry-leading conferences, experimentation and participating in our knowledge sharing community
  • Develop state‑of‑the‑art machine learning models to solve real‑world problems and apply them to tasks such as NLP, LLMs or recommendation systems
  • Produce outputs that lead to high‑impact business applications, open‑source software, patents, and publications in top AI/ML conferences and journals
  • Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production
  • Drive firm‑wide initiatives by developing large‑scale frameworks to accelerate the application of machine learning models across different areas of the business. The Machine Learning Center of Excellence (MCLOE) team partners across the firm to create and share machine‑learning solutions for our most challenging business problems. In this role, you will work and collaborate with a multi‑disciplinary community of experts focused exclusively on machine learning and work with cutting‑edge techniques in disciplines such as deep learning and reinforcement learning
    The candidate must excel in a highly collaborative environment with the business, technologists and control partners to deploy solutions into production. The candidate must also have a strong passion for machine learning and invest independent time toward learning, researching and experimenting with new innovations in the field. The candidate must possess solid expertise in deep learning with hands‑on implementation experience, strong analytical thinking, a deep desire to learn, and be highly motivated. 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 convey technical concepts and results to both technical and business audiences
  • Scientific thinking with the ability to invent and work both independently and in highly collaborative team environments
  • Solid written and spoken communication to effectively convey technical concepts and results to both technical and business audiences. Curious, hardworking and detail‑oriented, and motivated by complex analytical problems. 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
    The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm’s data and analytics journey. This includes ensuring the quality, integrity, and security of the company’s data, as well as leveraging this data to generate insights and drive decision‑making. The CDAO is also responsible for developing and implementing solutions that support the firm’s commercial goals by harnessing artificial intelligence and machine learning technologies to develop new products, improve productivity, and enhance risk management effectively and responsibly


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