トレンド企業

JPMorgan Chase
JPMorgan Chase

Global financial services firm

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

職種機械学習
経験リード級
勤務地LONDON, United Kingdom
勤務オンサイト
雇用正社員
掲載7ヶ月前
応募する

必須スキル

PyTorch

TensorFlow

Excel

Machine Learning

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
  • 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.: Tensor Flow, Py Torch, Num Py, 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

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.

#MLCOE_jobs

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JPMorgan Chaseについて

JPMorgan Chase

JPMorgan Chase & Co. is an American multinational banking institution headquartered in New York City and incorporated in Delaware. It is the largest bank in the United States, and the world's largest bank by market capitalization as of 2025.

300,000+

従業員数

New York City

本社所在地

$500B

企業価値

レビュー

10件のレビュー

3.8

10件のレビュー

ワークライフバランス

3.5

報酬

4.0

企業文化

3.8

キャリア

3.2

経営陣

2.8

68%

知人への推奨率

良い点

Good benefits and compensation

Supportive colleagues and environment

Flexible work arrangements

改善点

Long hours and heavy workload

Management issues and lack of direction

High stress and expectations

給与レンジ

44件のデータ

Mid/L4

Senior/L5

Mid/L4 · Applied AI ML Associate

2件のレポート

$188,500

年収総額

基本給

$145,000

ストック

-

ボーナス

-

$182,000

$195,000

面接レビュー

レビュー4件

難易度

3.0

/ 5

期間

14-28週間

内定率

50%

体験

ポジティブ 25%

普通 75%

ネガティブ 0%

面接プロセス

1

Application Review

2

HR Screen

3

Hiring Manager Interview

4

In-person/Final Interview

5

Offer

よくある質問

Behavioral/STAR

Past Experience

Culture Fit

Financial Knowledge

Case Study