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Applied AI ML Lead Engineer- (NLP/LLM/Graph)
lead · Technology / Software Development
NLP / LLM Scientist - Applied AI ML Lead - Machine Learning Centre of Excellence
The Machine Learning Center of Excellence invites the successful candidate to apply sophisticated machine learning methods to a wide variety of complex tasks including natural language processing, large language models, and recommendation systems.
The candidate must excel in working in a highly collaborative environment together 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 towards learning, researching and experimenting with new innovations in the field. The candidate must have solid expertise in Deep Learning with hands-on implementation experience and possess strong analytical thinking, a deep desire to learn and be highly motivated.
Job Responsibilities
- 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 it 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
- Develop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as natural language processing (NLP), speech recognition and analytics, time-series predictions or recommendation systems
- 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
- Research and explore new machine learning methods through independent study, attending industry-leading conferences, experimentation and participating in our knowledge sharing community
- Drive Firm wide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business
Required qualifications, capabilities, and skills
- 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
- Hands on experience building and deploying agentic AI / multi-agent systems within regulated or compliance-driven environments
- 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
Preferred qualifications, capabilities, and skills
- 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
About MLCOE
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 team comprised of a multi-disciplinary community of experts focused exclusively on Machine Learning. On this team you will work with cutting-edge techniques in disciplines such as Deep Learning and Reinforcement Learning
For more information about the MLCOE, please visit http://www.jpmorgan.com/mlcoe. To learn about how we're using AI/ML to drive transformational change, please read this blog: https://www.jpmorgan.com/insights/technology/technology-blog?source=cib_di_jp_aBtechblog102
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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