NLP / LLM Scientist - Applied AI ML Senior Associate - Machine Learning Centre of Excellence | [...] (Basé à London)

Jobleads
London
1 month ago
Create job alert

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

Job Description

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, speech analytics, time series, reinforcement learning 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 practiced 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, 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.


Required qualifications, capabilities, and skills

  • Solid background in NLP or speech recognition and analytics, personalization/recommendation and hands-on experience and solid understanding of machine learning and deep learning methods.
  • 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 industry or research experience in the field.
  • Applied 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.
  • Curious, hardworking and detail-oriented, and motivated by complex analytical problems.


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.
  • 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.
  • Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal.


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.

About Us

J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world's most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law.#J-18808-Ljbffr

Related Jobs

View all jobs

Applied Scientist - LLM, Alexa

Gen AI Engineer

ML/AI Software Engineer

ML/AI Software Engineer

Principal Data Scientist - NLP

Data Scientist, ISS

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Machine‑Learning Jobs for Non‑Technical Professionals: Where Do You Fit In?

The Model Needs More Than Math When ChatGPT went viral and London start‑ups raised seed rounds around “foundation models,” many professionals asked, “Do I need to learn PyTorch to work in machine learning?” The answer is no. According to the Turing Institute’s UK ML Industry Survey 2024, 39 % of advertised ML roles focus on strategy, compliance, product or operations rather than writing code. As models move from proof‑of‑concept to production, demand surges for specialists who translate algorithms into business value, manage risk and drive adoption. This guide reveals the fastest‑growing non‑coding ML roles, the transferable skills you may already have, real transition stories and a 90‑day action plan—no gradient descent necessary.

Quantexa Machine‑Learning Jobs in 2025: Your Complete UK Guide to Joining the Decision‑Intelligence Revolution

Money‑laundering rings, sanctioned entities, synthetic identities—complex risks hide in plain sight inside data. Quantexa, a London‑born scale‑up now valued at US $2.2 bn (Series F, August 2024), solves that problem with contextual decision‑intelligence (DI): graph analytics, entity resolution and machine learning stitched into a single platform. Banks, insurers, telecoms and governments from HSBC to HMRC use Quantexa to spot fraud, combat financial crime and optimise customer engagement. With the launch of Quantexa AI Studio in February 2025—bringing generative AI co‑pilots and large‑scale Graph Neural Networks (GNNs) to the platform—the company is hiring at record pace. The Quantexa careers portal lists 450+ open roles worldwide, over 220 in the UK across data science, software engineering, ML Ops and client delivery. Whether you are a graduate data scientist fluent in Python, a Scala veteran who loves Spark or a solutions architect who can turn messy data into knowledge graphs, this guide explains how to land a Quantexa machine‑learning job in 2025.

Machine Learning vs. Deep Learning vs. MLOps Jobs: Which Path Should You Choose?

Machine Learning (ML) continues to transform how businesses operate, from personalised product recommendations to automated fraud detection. As ML adoption accelerates in nearly every industry—finance, healthcare, retail, automotive, and beyond—the demand for professionals with specialised ML skills is surging. Yet as you browse Machine Learning jobs on www.machinelearningjobs.co.uk, you may encounter multiple sub-disciplines, such as Deep Learning and MLOps. Each of these fields offers unique challenges, requires a distinct skill set, and can lead to a rewarding career path. So how do Machine Learning, Deep Learning, and MLOps differ? And which area best aligns with your talents and aspirations? This comprehensive guide will define each field, highlight overlaps and differences, discuss salary ranges and typical responsibilities, and explore real-world examples. By the end, you’ll have a clearer vision of which career track suits you—whether you prefer building foundational ML models, pushing the boundaries of neural network performance, or orchestrating robust ML pipelines at scale.