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

JPMorgan Chase & Co.
London
6 days ago
Create job alert

  • 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


#J-18808-Ljbffr

Related Jobs

View all jobs

Senior NLP & LLM Scientist for Financial Analytics

NLP/LLM Research Scientist (PhD) – Cambridge Hybrid

Remote NLP & LLM Data Scientist for AI Analytics Platform

Data Scientist (NLP & LLM Specialist)

AI/ML Data Scientist - NLP & LLM in Production

Data Scientist (NLP & LLM Specialist)

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.