Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Join a team to innovate AI solutions for banking, optimising decisions and automating processes.
- Company: J.P. Morgan is a global leader in financial services, serving top corporations and governments.
- Benefits: Enjoy a diverse workplace with opportunities for growth and flexible working arrangements.
- Why this job: Be at the cutting edge of AI in finance, making impactful contributions to real-world applications.
- Qualifications: PhD in a quantitative field and hands-on ML engineering experience required.
- Other info: Diversity and inclusion are core values; we welcome applicants from all backgrounds.
The predicted salary is between 43200 - 72000 Ā£ per year.
Join a high performing team of applied AI experts to drive innovation and new capabilities in the Commercial & Investment Bank.
As an Applied AI / ML Senior Associate Machine Learning Engineer in the Applied AI ML team at JPMorgan Commercial & Investment Bank, you will be at the forefront of combining cuttingāedge AI techniques with the company\ās unique data assets to optimize business decisions and automate processes. You will have the opportunity to advance the stateāofātheāart 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 AIāpowered products and services and increasing opportunity for reāuse 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.
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.
Act as an individual contributor, though there will be optional opportunity for management responsibility dependent on the candidate\ās experience.
Required qualifications, capabilities, and skills
Masters or PhD in a quantitative discipline, e.g. Computer Science, Mathematics, Statistics
Solid understanding of fundamentals of statistics, optimization and ML theory. Familiarity with popular deep learning architectures (transformers, CNN, autoencoders etc.)
Specialism or wellāresearched interest in NLP
Broad knowledge of MLOps tooling ā for versioning, reproducibility, observability etc.
Experience monitoring, maintaining, enhancing existing models over an extended time period
Extensive experience with PyTorch and related data science Python libraries (e.g. Pandas)
Experience of containerising applications or models for deployment (Docker)
Experience with one of the major public cloud providers (Azure, AWS, GCP)
Ability to communicate technical information and ideas at all levels; convey information clearly and create trust with stakeholders.
Preferred qualifications, capabilities, and skills
Experience designing/implementing pipelines using DAGs (e.g. Kubeflow, DVC, Ray)
Experience of big data technologies
Have constructed batch and streaming microservices exposed as REST/gRPC endpoints
Experience with container orchestration tools (e.g. Kubernetes, Helm)
Knowledge of open source datasets and benchmarks in NLP
Handsāon experience in implementing distributed/multiāthreaded/scalable applications
Track record of developing, deploying businessācritical machine learning models
#CIBAppliedAI
#J-18808-Ljbffr
Contact Detail:
Jpmorgan Chase & Co. Recruiting Team
StudySmarter Expert Advice š¤«
We think this is how you could land Applied AI ML - Senior Associate - Machine Learning Engineer
āØTip Number 1
Familiarise yourself with the latest advancements in AI and machine learning, particularly in Natural Language Processing and Computer Vision. This will not only help you understand the role better but also allow you to engage in meaningful conversations during interviews.
āØTip Number 2
Network with professionals in the field of applied AI and ML, especially those working in financial services. Attend relevant meetups or webinars to gain insights and potentially get referrals that could boost your application.
āØTip Number 3
Showcase your hands-on experience with MLOps tools and frameworks in your discussions. Being able to talk about specific projects where you've implemented these technologies can set you apart from other candidates.
āØTip Number 4
Prepare to discuss how you've communicated complex technical concepts to non-technical stakeholders in the past. This skill is crucial for the role, and demonstrating your ability to bridge that gap can make a strong impression.
We think you need these skills to ace Applied AI ML - Senior Associate - Machine Learning Engineer
Some tips for your application š«”
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning engineering, particularly any hands-on projects or roles that align with the job description. Emphasise your PhD and any specific skills in NLP or Computer Vision.
Craft a Compelling Cover Letter: Write a cover letter that connects your background to the responsibilities of the role. Discuss your experience with MLOps tooling and how you've successfully deployed machine learning models in previous positions.
Showcase Technical Skills: In your application, clearly outline your technical skills, especially your proficiency with tools like PyTorch, NumPy, and Pandas. Mention any experience with big data technologies and distributed applications, as these are crucial for the role.
Prepare for Interviews: Be ready to discuss your past projects in detail, particularly those involving statistical analysis and machine learning techniques. Prepare to explain complex concepts in a way that is accessible to non-technical stakeholders, as communication is key in this role.
How to prepare for a job interview at Jpmorgan Chase & Co.
āØShowcase Your Technical Skills
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.
āØUnderstand the Business Context
Demonstrate your understanding of how AI can optimise business decisions in financial services. Be ready to explain how your technical skills can directly contribute to the goals of the Commercial & Investment Bank.
āØCommunicate Clearly
Since you'll be communicating with both technical and non-technical audiences, practice explaining complex concepts in simple terms. This will help build trust with stakeholders and show your ability to bridge the gap between tech and business.
āØPrepare for Collaboration Questions
Expect questions about your experience working with cross-functional teams, such as software engineering and cloud teams. Be ready to share examples of how you've successfully collaborated on projects and contributed to production architectures.
