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JOB DESCRIPTION
Ignite your passion for product innovation by leading customer-centric development, inspiring solutions, and shaping the future with your strategic vision and influence.
As an Executive Director for AI Product Management Transformation in Product Operations within Infrastructure Platforms, you lead innovation through the development of products and features that delight customers. As a leader on the team, you leverage your advanced capabilities to challenge traditional approaches, remove barriers to success, and foster a culture of continuous innovation that helps inspire cross-functional teams create groundbreaking solutions that address customer needs.
Job responsibilities
- Oversees the product roadmap, vision, development, execution, risk management, and business growth targets
- Leads the entire product life cycle through planning, execution, and future development by continuously adapting, developing new products and methodologies, managing risks, and achieving business targets like cost, features, reusability, and reliability to support growth
- Coaches and mentors the product team on best practices, such as solution generation, market research, storyboarding, mind-mapping, prototyping methods, product adoption strategies, and product delivery, enabling them to effectively deliver on objectives
- Owns product performance and is accountable for investing in enhancements to achieve business objectives
- Monitors market trends, conducts competitive analysis, and identifies opportunities for product differentiation
- Own the product management best practices for the organization. Establish and lead the Product Management Center of Excellence, including the best practices, templates, playbooks, and governance that define how infrastructure platforms are managed as products. These practices span the full product lifecycle, from understanding markets and customers through strategy and planning, iterative delivery, launch, scaling, and eventual retirement, covering approximately two dozen disciplines across research, strategy, delivery, enablement, operations, and governance. Accountable for ensuring these practices exist, remain current, and are applied consistently across product areas
- Elevate product management as a discipline across the product management community. Drive coaching, training, career development, and a community of practice; increase measured product management maturity; and clarify responsibilities among product management, engineering, and architecture so product managers can focus on strategy, planning, and value delivery
- Operationalize artificial intelligence across the product management lifecycle. Own the portfolio of AI assistants and automations mapped to each phase of the lifecycle, along with the governance, instruction management, and consumption cost controls that make them safe and affordable at scale. Accountable for the capacity, decision quality, and cycle-time outcomes these solutions are funded to achieve, as well as determining which use cases deliver meaningful business value
- Partner with the product management community and the broader technology organization. Product managers are the primary stakeholders and are both the users of these practices and the key drivers of their adoption. Earning their trust and confidence is critical to success. Partner closely with teams across the firm's business units, platform engineering, architecture, program execution, enterprise strategy, and shared product tooling organizations. Convene forums where demand, ownership, and priorities are aligned. Drive adoption through influence and partnership rather than mandate
- Embed business, risk, and regulatory alignment into the practices themselves. Ensure that risk, security, regulatory, control, and cost considerations are built into how products are managed rather than addressed through downstream reviews. Includes accountability for the function's ability to demonstrate and evidence its controls
Lead the team and measure adoption rather than publication. Build and develop the team while establishing measures of adoption and maturity so that practices are evaluated based on uptake and improved outcomes. A practice that is not used is not a delivered practice *
Required qualifications, capabilities, and skills
- 8+ years of experience or equivalent expertise delivering products, projects, or technology applications
- Extensive knowledge of the product development life cycle, technical design, and data analytics
- Proven ability to influence the adoption of key product life cycle activities including discovery, ideation, strategic development, requirements definition, and value management
- Experience driving change within organizations and managing stakeholders across multiple functions
- Experience leading a product management function, practice, or Center of Excellence
- Experience managing platform or infrastructure products serving internal customers
- Experience building and scaling enterprise product management practices, operating models, or Centers of Excellence
- Experience coaching, mentoring, and developing product managers
- Hands-on experience developing AI-enabled assistants, automations, or workflows and evaluating high-value use cases for adoption
- Experience operating within financial services or another highly regulated industry
Preferred qualifications, capabilities, and skills
- Recognized thought leader within a related field
- Understanding of the different technologies across the infrastructure stack, including compute, storage, networking, cloud platforms, developer tooling and shared services. Enough to hold a credible conversation with engineering about any of them
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 $180K-$285K 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 ($232K) sits 10% above the category median. Disclosed range: $180K to $285K.
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
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 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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