Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Lead AI projects to optimise business decisions and automate processes in finance.
- Company: Join JPMorgan, a global leader in financial services, driving innovation with AI.
- Benefits: Enjoy flexible work options, competitive salary, and opportunities for professional growth.
- Why this job: Be at the forefront of AI in finance, making impactful decisions and advancing technology.
- Qualifications: PhD in a quantitative field and hands-on ML engineering experience required.
- Other info: Opportunity for management roles based on experience; collaborate with top tech teams.
The predicted salary is between 57600 - 84000 £ per year.
Take a technical leadership position within JPMorgan's Commercial & Investment Bank, where you'll harness cutting-edge AI techniques to revolutionize business decisions and automate processes. As an Applied AI / ML Lead – Vice President - Machine Learning Engineer in the Applied AI ML team at JPMorgan Commercial & Investment Bank, you will be at the forefront of combining cutting-edge AI techniques with the company's unique data assets to optimize business decisions and automate processes. You will have the opportunity to advance the state-of-the-art in AI as applied to financial services, leveraging the latest research from fields of Natural Language Processing, Computer Vision, and statistical machine learning.
You will be instrumental in building products that automate processes, help experts prioritize their time, and make better decisions. We have a growing portfolio of AI–powered products and services and increasing opportunity for re-use of foundational components through careful design of libraries and services to be leveraged across the team. This role offers a unique blend of scientific research and software engineering, requiring a deep understanding of both mindsets. The role is initially that of an individual contributor, though there will be optional opportunity for management responsibility dependent on the candidate’s experience.
Job responsibilities- Build robust Data Science capabilities which can be scaled across multiple business use cases
- Collaborate with software engineering team to design and deploy Machine Learning services that can be integrated with strategic systems
- Research and analyse data sets using a variety of statistical and machine learning techniques
- Communicate AI capabilities and results to both technical and non-technical audiences
- Document approaches taken, techniques used and processes followed to comply with industry regulation
- Collaborate closely with cloud and SRE teams while taking a leading role in the design and delivery of the production architectures for our solutions
- Hands on experience in an ML engineering role
- PhD in a quantitative discipline, e.g. Computer Science, Mathematics, Statistics
- Track record of developing, deploying business critical machine learning models
- Broad knowledge of MLOps tooling – for versioning, reproducibility, observability etc
- Experience monitoring, maintaining, enhancing existing models over an extended time period
- Specialism in NLP or Computer Vision
- Solid understanding of fundamentals of statistics, optimization and ML theory
- Familiarity with popular deep learning architectures (transformers, CNN, autoencoders etc.)
- Extensive experience with pytorch, numpy, pandas
- Knowledge of open source datasets and benchmarks in NLP / Computer Vision
- Hands-on experience in implementing distributed/multi-threaded/scalable applications (incl. frameworks such as Ray, Horovod, DeepSpeed, etc.)
- Able to communicate technical information and ideas at all levels; convey information clearly and create trust with stakeholders
- Experience designing/ implementing pipelines using DAGs (e.g. Kubeflow, DVC, Ray)
- Experience of big data technologies (e.g. Spark, Hadoop)
- Have constructed batch and streaming microservices exposed as REST/gRPC endpoints
- Familiarity with GraphQL
Applied AI ML Lead - Senior Machine Learning Engineer - Commercial and Investment Bank employer: J.P. Morgan
Contact Detail:
J.P. Morgan Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Applied AI ML Lead - Senior Machine Learning Engineer - Commercial and Investment Bank
✨Tip Number 1
Network with professionals in the finance and AI sectors. Attend industry conferences, webinars, or meetups to connect with people who work at JPMorgan or similar companies. This can give you insights into the company culture and potentially lead to referrals.
✨Tip Number 2
Stay updated on the latest advancements in AI and machine learning, particularly in financial services. Follow relevant research papers, blogs, and podcasts to discuss these topics during interviews, showcasing your passion and knowledge.
✨Tip Number 3
Prepare to demonstrate your technical skills through practical assessments or coding challenges. Brush up on your proficiency with tools like PyTorch, and be ready to discuss your experience with MLOps and model deployment strategies.
✨Tip Number 4
Familiarise yourself with JPMorgan's AI initiatives and products. Understanding their current projects and how they leverage AI will help you tailor your discussions and show that you're genuinely interested in contributing to their goals.
We think you need these skills to ace Applied AI ML Lead - Senior Machine Learning Engineer - Commercial and Investment Bank
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning engineering, particularly any hands-on work with NLP or Computer Vision. Emphasise your PhD and any projects that demonstrate your ability to develop and deploy business-critical ML models.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI and how your skills align with the role at JPMorgan. Mention specific projects or experiences that showcase your expertise in MLOps tooling and your ability to communicate complex technical concepts to diverse audiences.
Showcase Your Technical Skills: Include a section in your application that lists your technical proficiencies, such as experience with PyTorch, NumPy, and Pandas. Highlight any familiarity with big data technologies and distributed systems, as these are crucial for the role.
Prepare for Technical Questions: Anticipate technical questions related to machine learning theory, statistics, and optimisation. Be ready to discuss your experience with monitoring and maintaining ML models, as well as your understanding of deep learning architectures.
How to prepare for a job interview at J.P. Morgan
✨Showcase Your Technical Expertise
Be prepared to discuss your hands-on experience in machine learning engineering. Highlight specific projects where you've developed and deployed business-critical models, especially in NLP or Computer Vision.
✨Demonstrate Collaboration Skills
Since the role involves working closely with software engineering and cloud teams, share examples of past collaborations. Emphasise how you effectively communicated technical concepts to both technical and non-technical audiences.
✨Discuss MLOps Knowledge
Familiarity with MLOps tooling is crucial. Be ready to talk about your experience with versioning, reproducibility, and observability in machine learning projects, and how these practices can enhance model performance over time.
✨Prepare for Problem-Solving Questions
Expect questions that assess your problem-solving abilities in real-world scenarios. Think about challenges you've faced in previous roles and how you applied statistical and machine learning techniques to overcome them.
