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JOB DESCRIPTION
We are scaling artificial intelligence across a global enterprise — moving from experimentation to durable, realized business value. Our AI Transformation Community of Practice (CoP) exists to accelerate the transformations owned by our senior AI Transformation Leads, who (along with our business and function executives) set the aspiration and translate it into measurable business outcomes, shape the transformation portfolio, and enable delivery at pace. The goal of the community is to enable the AI Transformation Leads to reach the right capabilities, expertise, and assets faster, remove blockers, increase reuse across businesses and functions, and move from ambition to execution and value. The community will consist of a flagship summit, recurring deep-dive sessions, and an always-on virtual community. These are the mechanisms; accelerating these leaders is the goal.
As an Executive Director, AI Transformation Community of Practice (CoP) in the enterprise AI Skilling & Enablement organization, you own the learning spine of our AI Transformation Leads CoP: converting what we learn from those leaders into sharper CoP learning offerings and experiences, and into reusable patterns and insights that you feed to the teams developing our enterprise AI transformation playbook and delivering end-to-end transformation programs with businesses and functions. You will hold overall accountability for the community and partner closely with the Vice President, AI CoP Program Manager (reporting to you), who owns operating and delivery execution, and you will be measured on leader outcomes: are we solving their real needs, getting them to the right capabilities faster, removing barriers, and increasing reuse? You will report directly to the Head of AI Skilling & Enablement, and you will partner closely with our strategy teams serving businesses and functions on AI transformation.
This role carries significant strategic weight: it sets the learning agenda for the community and ensures that every learning touchpoint — the flagship summit, deep-dive sessions, and virtual community — moves senior leaders from inspiration to applied practice. You must be credible in the room with senior executives and able to hold substantive, peer-level conversations with AI Transformation Leads and enterprise capability owners on end-to-end AI transformation.
Job Responsibilities
- Develop and own the learning strategy for the community — what we teach and how we teach it — so that AI Transformation Leads are equipped with the toolkit to deliver AI transformation at scale and measurable business outcomes.
- Synthesize what we learn from AI Transformation Leads into concrete recommendations for capability owners, such as our enterprise data and AI platform and workforce design teams, and into AI transformation playbook updates owned by the strategy team, working from the recurring patterns you and the Program Manager observe and surface.
- Design senior-leader learning experiences that measurably move leaders' initiatives forward, scoped from the live transformation problems those leaders surface.
- Own the flagship summit's content architecture while the Program Manager drives delivery: source keynote speakers, shape exhibits and hackathons, and curate the case studies, demos, and reusable assets we showcase in our learning programs and resources.
- Curate the recurring deep-dive learning slate starting from the problems leaders are actually stuck on; sequence sessions against those needs and refresh as demand shifts; and partner with enterprise capability owners and business leaders to confirm the right experts for each session.
- Define and curate the always-on digital community as the front door that helps leaders find the right capability, the right experts, and a curated catalog of offerings, while the Program Manager runs the platform and moderation.
- Identify the senior-leader learning content we are missing, such as organization design and scaled adoption and change management topics, and partner with the AI Transformation playbook and senior-leader curriculum teams to address and close the gaps.
- Codify what we learn into a CoP learning approach consistent with our AI transformation playbook methodology, and establish the measurement framework — time to capability, barriers removed, cross-business reuse, value realized — against which the community will be judged.
- Partner with our executive development colleagues as their content counterpart; co-design joint offerings and align on measurement so senior-leader engagement connects to transformation execution.
Required Qualifications, Capabilities, and Skills
- 10 years of experience across senior-audience program content, community, or knowledge-product ownership and hands-on transformation or strategy consulting.
- Experience designing and evaluating learning curricula for an executive audience, including scoping, design, and measurement of outcomes.
- Experience owning a body of intellectual content end to end, such as a playbook, methodology, curriculum, or enterprise knowledge product.
- Hands-on experience applying generative and agentic AI tools in your own work, and a documented point of view on enterprise AI transformation.
- Experience running needs assessments, external benchmarking, and survey design, and analyzing outcome and business-impact data.
- Experience writing and editing for an executive audience and facilitating senior-level discussion, including preparing executive speakers.
- Experience working across human resources, strategy, and business partners on a shared agenda, and willingness to travel up to 20 percent for flagship events.
Preferred Qualifications, Capabilities, and Skills
- Prior ownership of a knowledge product or senior-audience community of practice.
- Prior experience delivering executive development curriculum in partnership with a dedicated executive development function.
- Experience with AI transformation frameworks or delivering reimagined, AI-native workflows.
- Product sense for a community platform — defining the front-door experience, curation criteria, and moderation norms.
- Familiarity with a regulated environment and its governance expectations.
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
Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.
Salary Context
This $183K-$294K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($238K) sits 13% above the category median. Disclosed range: $183K to $294K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
JPMorganChase AI Hiring
JPMorganChase has 178 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Product Manager, AI Software Engineer. Positions span San Francisco, CA, US, Jersey City, NJ, US, New York, NY. Compensation range: $155K - $325K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% above 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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