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About This Role
JOB DESCRIPTION
In the Data \& AI Program at JPMorganChase, you'll drive impact by building end\-to\-end data, analytics, and artificial intelligence and machine learning solutions that translate business objectives into measurable outcomes for clients and customers. Working alongside global experts in agile teams, you will design scalable data platforms and pipelines, develop production\-ready models, create intuitive dashboards, and ensure strong data governance, privacy, and compliance.
Leveraging modern tools (e.g., AWS, CoPilot, Snowflake, DataBricks, LLM), you will integrate diverse datasets, run experiments tied to key performance indicators, and deliver actionable insights. This program provides hands\-on experience, mentorship, and training in a collaborative, innovation\-focused environment—positioning you to contribute to high\-impact initiatives across the firm while building a strong foundation in Data \& AI.
ABOUT THE PROGRAM
As a Summer Analyst in the Data \& AI Program at JPMorganChase, you will help drive transformation by combining your technical skills, business context, and modern tools to deliver real\-world impact for our clients and customers.
You will collaborate with global experts to develop end\-to\-end data and AI solutions and contribute to positive change for the diverse communities we serve. You will receive mentorship, training, and support as you build your career in a culture where we value your ideas and help you grow.
We will be filling our classes on a rolling basis. We strongly encourage you to submit your application as early as possible before job postings close.
JOB RESPONSIBILITIES
- Build intelligent systems that power real business outcomes, from machine learning models to generative artificial intelligence and agent\-based solutions.
- Work hands\-on with cutting\-edge technologies to design, develop, and deploy artificial intelligence capabilities that automate processes and enhance decision\-making.
- Apply data, statistics, and modeling to solve complex problems and generate actionable insights.
- Develop predictive models, test hypotheses, and analyze trends to help teams make smarter, data\-driven decisions.
- Support data governance, risk management, and data standards to help ensure data is secure, trusted, and ready for artificial intelligence use.
- Implement controls, improve data quality, and enable frameworks and structures that make solutions safe, scalable, and effective
- Collaborate in agile teams and contribute ideas from day one in a culture that supports your growth and impact.
- Learn through mentorship and training while building a strong foundation in data, analytics, and artificial intelligence.
REQUIRED QUALIFICATIONS, CAPABILITIES AND SKILLS
- Pursuing a Bachelor's or Master's degree in a quantitative or technical discipline (e.g., Data Science, Machine Learning, Computer Science, or Mathematics).
- Graduating between December 2027 and August 2028\.
- Authorized to work permanently in the United States.
- Meeting the role requirement that no prior work experience is needed.
- No prior work experience is required.
PREFERRED QUALIFICATIONS, CAPABILITIES, AND SKILLS
- Demonstrates strong knowledge of machine learning, data science principles, including prompt engineering, with experience handling large, complex datasets.
- Use programming languages such as SQL and Python.
- Use data \& artificial intelligence tools (e.g., AWS, CoPilot, Snowflake, DataBricks, LLM).
- Understand data management and governance, including data platforms, pipelines, models, taxonomies, metadata, lineage, privacy, and regulatory compliance.
- Apply strong quantitative and analytical problem\-solving skills to design experiments and deliver measurable outcomes (e.g., key performance indicators, uplift, return on investment).
- Communicate clearly in writing and verbally to translate technical work for business stakeholders and collaborate across agile, cross\-functional teams.
- Translate business objectives into testable hypotheses and analytical plans, develops models and experiments, and communicates actionable recommendations to stakeholders.
This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorganChase's review of criminal conviction history, including pretrial diversions or program entries.
LOCATIONS YOU MAY JOIN:
- New York Metro
- Columbus, OH
- Chicago, IL
- Delaware Metro
- Plano, TX
- Palo Alto, CA
ABOUT US
At JPMorganChase, Data \& AI sits at the core of how we operate, innovate, and serve our clients. We harness advanced data, analytics, and artificial intelligence to drive better decisions, power products, and deliver real\-world impact across global markets.
Our teams work at the intersection of technology and business—developing end\-to\-end data platforms, applying machine learning, and generating insights that shape everything from client experiences to risk management and operations. From building scalable data infrastructure to deploying AI models and visualization tools, we transform complex data into actionable intelligence.
Data \& AI at JPMC is not experimental—it is embedded across the firm. Our capabilities support critical functions including trading, fraud prevention, wealth management, and cybersecurity, delivering measurable value at enterprise scale.
We operate on modern, secure platforms that enable AI and analytics at scale, providing governed environments where teams can build, analyze, and deploy solutions responsibly. This allows us to combine technical excellence with strong governance, privacy, and risk management standards.
Collaboration is at the heart of how we work. Our Data \& AI professionals partner across lines of business and disciplines—bringing together engineers, data scientists, and domain experts to solve complex problems and deliver meaningful outcomes for clients and communities worldwide.
As part of the Data \& AI function at JPMorganChase, you'll contribute to shaping the future of financial services—working on high\-impact solutions in an environment that values innovation, continuous learning, and real\-world application of cutting\-edge technologies.
ABOUT YOU
If you're ready to put your passion for data and artificial intelligence to work in a way that makes a real difference, you'll find your place in our Data \& AI Program.
*To be eligible for this program, you must be authorized to work in the U.S. We do not offer any type of employment\-based immigration sponsorship for this program. Likewise, JPMorgan Chase, will not provide any assistance or sign any documentation in support of any other form of immigration sponsorship or benefit including optional practical training (OPT) or curricular practical training (CPT.)*
WHAT'S NEXT?
Help us learn about you by submitting a complete and thoughtful application, which includes your resume. Your application and resume is a way for us to initially get to know you, so it's important to complete all relevant application questions so we have as much information about you as possible.
After you confirm your application, we will review it to determine whether you meet certain required qualifications.
JPMorganChase is committed to creating an inclusive work environment that respects all people for their unique skills, backgrounds and professional experiences. We will provide reasonable accommodations for applicants with disabilities.
Visit jpmorganchase.com/careers for upcoming events, career advice, our locations and more.
©2026 JPMorgan Chase \& Co. JPMorgan Chase is an equal opportunity and affirmative action employer Disability/Veteran
ABOUT US
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
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. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At JPMorganChase, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
JPMorganChase AI Hiring
JPMorganChase has 141 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span Jersey City, NJ, US, New York, NY, US, Seattle, WA, US. Compensation range: $120K - $450K.
Location Context
AI roles in Chicago pay a median of $192,900 across 197 tracked positions. That's 10% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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