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About This Role
JOB DESCRIPTION
You enjoy shaping the future of product innovation as a core leader, driving value for clients, guiding successful launches, and exceeding expectations.
You are joining the JPMorgan Payments Data \& Analytics team at the start of something new. Our team is building an Agentic Treasury (AT) capability for our clients \- a suite of AI agents that will automate and augment how corporate treasurers manage cash management, receivables, payables, cash flow forecasting, and working capital decisions. You will take us on a 0\-1 journey from proof\-of\-concept to production\-grade, client\-ready systems, and you will own the product thinking that makes that happen. You will be embedded in the Payments ecosystem, working hand\-in\-hand with colleagues from functions like data science, engineering, design, legal, etc. You would help translate a complex, multi\-stakeholder landscape into a coherent agent product that only JPMorgan can build.
As a Product Manager, Agentic Treasury\- Payments Data \& Analytics in our Payments business, you are an integral part of the team that innovates new product offerings and leads the end\-to\-end product life cycle. As a core leader, you are responsible for acting as the voice of the client and developing profitable products that provide client value. Utilizing your deep understanding of how to get a product off the ground, you guide the successful launch of products, gather crucial feedback, and ensure top\-tier client experiences. With a strong commitment to scalability, resiliency, and stability, you collaborate closely with cross\-functional teams to deliver high\-quality products that exceed client expectations.
Job responsibilities
- Own end\-to\-end product management for the AT initiative \- from Proof\-of\-Concept scoping and design through iterative build, internal validation, and eventual production\-grade client delivery
- Define and maintain the AT product roadmap, translating organizational strategy, partner team inputs, and emerging agentic AI capabilities into a sequenced, prioritized backlog
- Partner deeply with internal Payments business lines to align on data availability, capability dependencies, API integration points, and go\-to\-market sequencing
- Lead cross\-functional delivery across data science, engineering, design, legal, compliance, and risk \- driving alignment on scope, acceptance criteria, and release readiness
- Define agent behavior specifications: what data each agent consumes, what decisions it makes or recommends, what human\-in\-the\-loop checkpoints are required, and how performance is measured
- Own documentation rigor: product requirements, agent logic specs, partner integration playbooks, and decision logs that make complex interdependencies legible across a large organization
- Drive sprint\-level execution using agile methodologies \- backlog grooming, sprint planning, standup, and retrospective \- keeping a distributed, multi\-team effort on track
- Establish and track product KPIs across the AT agent suite: automation rates, exception volumes, STP improvement, time\-to\-resolution, and adoption metrics that demonstrate business impact
- Communicate product status, trade\-offs, and strategic decisions clearly upward to senior leadership and laterally across partner teams
Required qualifications, capabilities, and skills
- 5\+ years of product management experience in a complex, cross\-functional environment \- you have shipped products that required navigating multiple internal stakeholders with competing priorities
- Proven ability to move from ambiguous problem space to structured product definition \- you have written the PRD when there was no template, and built the roadmap when there was no precedent
- Strong command of agile delivery: you have managed engineers, run sprints, unblocked teams, and made real prioritization calls under pressure
- Demonstrated experience working across organizational boundaries \- business, technology, legal, risk, compliance \- to get something built and launched
- Excellent written and verbal communication \- you can write a crisp one\-pager for a senior executive and turn around a detailed spec for an engineer
- Bachelor's degree in a quantitative, technical or business discipline
Preferred qualifications, capabilities, and skills
- Experience in financial services, payments, or enterprise treasury/fintech \- familiarity with how corporate clients think about cash positioning, AR/AP workflows, and working capital
- Exposure to AI/ML product development – you have worked alongside data scientists, understand what "agentic" systems are, and have a point of view on what production\-ready AI behavior looks like
- Familiarity with payment rails (ACH, RTP, FedNow, SWIFT, Fedwire) or treasury management platforms (Kyriba, ION, SAP TRM, FIS) from a product or implementation perspective
- Prior experience at a management consulting firm, fintech, or enterprise software company where ambiguity was the default condition
- Ability to engage technical depth \- Python, SQL, or equivalent \- sufficient to interrogate data, validate logic, and have credible conversations with engineering partners
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 offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission\-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on\-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
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.
JPMorgan Chase \& Co. is an Equal Opportunity Employer, including Disability/Veterans
ABOUT THE TEAM
A part of the Commercial \& Investment Bank, J.P. Morgan Payments enables organizations of all sizes to execute transactions efficiently and securely, transforming the movement of information, money, and assets. The team of experts tackles complex challenges at every stage of the payment lifecycle. And their industry\-leading solutions facilitate seamless transactions across borders, industries, and platforms.
Operating in over 160 countries and handling more than 120 currencies, J.P. Morgan Payments business is the largest processor of USD payments, with a daily transaction volume of $10 trillion.
Salary Context
This $122K-$201K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. This role's midpoint ($161K) sits 26% below the category median. Disclosed range: $122K to $201K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
JPMorganChase AI Hiring
JPMorganChase has 88 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, Columbus, OH, US, New York, NY, US. Compensation range: $130K - $325K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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