Interested in this AI/ML Engineer role at JPMorganChase?
Apply Now →About This Role
JOB DESCRIPTION
If you enjoy turning complex, cross\-organization challenges into measurable outcomes, this role offers the opportunity to modernize how risk and control work gets done. You will lead high\-impact transformation programs and help teams adopt practical, responsible AI and automation\-enabled ways of working. You'll partner across lines of defense and business teams to improve effectiveness, consistency, transparency, and capacity while balancing risk, privacy, and control requirements. You will help teams adopt scalable approaches that work across diverse business areas and operating models.
As a Vice President, Program Manager – Innovation \& AI Transformation in the Controls Room team, you will lead cross\-functional transformation programs that improve control management outcomes by redesigning workflows and embedding AI and automation where they accelerate impact. The Controls Room, part of Control Management, strengthens existing control functions and develops new ones by monitoring firmwide, risk\-based intelligence that empowers the three lines of defense. You will translate opportunities into clear roadmaps with measurable outcomes and run governance from pilot through scale, managing dependencies and escalating where broader alignment is needed. You will design solutions across people, process, operating model, data, and technology while balancing time\-to\-value with risk and control requirements.
Control Management
Control Managers are responsible for having a deep understanding of the business, its underlying processes and the compliance and operational risk and control environment. Subject matter expertise/innovative tools and technology.
Control Management maintains a strong and consistent control environment across the firm. With Control Managers appointed for each Line of Business, Function and Region, there is a comprehensive coverage and joint accountability model with the business executive that promotes early operational risk identification and assessment, effective design and evaluation of controls and sustainable solutions to mitigate operational risk.
Controls Room
The Controls Room Team within Control Management works collaboratively with other control disciplines and oversees existing control functions as well as the development of new control frameworks, protocols, and tooling solutions. The team's objective is to empower the three lines of defense with risk\-based information to manage their control environment. This is achieved by collecting, analyzing and monitoring firmwide risk\-based intelligence into all lines of business and corporate functions.
The Controls Room gathers information from key business and technology partners in order to:
- Facilitate better decision making and actionable insights across the Firm.
- Create cost\-effective and automated reporting; and
- Provide views into risk and issues.
Job Responsibilities:
- Lead AI\-enabled transformation programs across control management processes by embedding AI and automation into defined workflows.
- Diagnose end\-to\-end workflows to identify root causes, baseline performance, and define clear outcomes and key performance indicators.
- Discover, size, and shape use cases and translate them into executable roadmaps with requirements, milestones, and success measures.
- Design fit\-for\-purpose solutions across people, process, operating model, data, and technology, clearly articulating trade\-offs across cost, complexity, risk, and time\-to\-value.
- Define and implement operating model changes that embed AI into day\-to\-day work, including roles, handoffs, escalation paths, exception handling, and human review points.
- Own program delivery and governance, including plans, dependencies, risks and issues, decision logs, forums, and executive\-ready status updates.
- Drive decisions and escalations to maintain momentum, remove blockers, and deliver outcomes on time and with quality.
- Partner across lines of defense, lines of business, and corporate functions to align priorities and execute responsibly within established forums.
- Track benefits and value realization by measuring performance against targets and continuously improving based on feedback and data.
- Produce periodic and ad\-hoc roadmaps, reporting, and dashboards to meet stakeholder needs across the organization.
Required Qualifications, Capabilities and Skills:
- Demonstrated program management experience delivering cross\-functional transformation initiatives from discovery through implementation, typically demonstrated through 7\+ years of relevant experience.
- Strong command of current AI and automation technologies, with a demonstrated commitment to tracking emerging tools and approaches that keep solutions innovative and forward\-looking.
- Strong structured problem\-solving skills (including root\-cause analysis, process mapping, and translating ambiguity into clear plans).
- Experience defining outcomes, metrics, and key performance indicators, and using data to manage progress and impact.
- Demonstrated ability to apply AI and automation to knowledge and workflow\-heavy processes (e.g., triage, knowledge enablement, assisted drafting, summarization, and quality monitoring).
- Ability to design solutions across people, process, operating model, data, and technology, including clear documentation of requirements and trade\-offs.
- Strong stakeholder management and influencing skills across senior and cross\-functional partners, including decision facilitation.
- Strong governance and delivery discipline, including dependency management, risk and issue management, and executive reporting.
- Change leadership capability, including adoption planning, communications, training, and sustainment.
- Risk and controls mindset, including comfort partnering with control stakeholders and incorporating privacy and compliance requirements into delivery.
Preferred Qualifications, Capabilities and Skills:
- Experience working in control, audit, compliance, operational risk, or a closely related function at a large financial institution.
- Proven success scaling AI or automation initiatives from pilot to production with monitoring and feedback loops.
- Track record of delivering AI responsibly in a regulated environment (e.g., model governance considerations, documentation standards, and human\-in\-the\-loop review).
- Working command of continuous improvement methods (e.g., Lean Six Sigma) and practical process redesign.
- Skilled at building business cases and value frameworks and tracking benefits realization post\-implementation.
- Hands\-on experience rapidly prototyping with AI coding assistants to create demos or lightweight solutions, demonstrating initiative to learn tools directly and translate ideas into working outputs.
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.
Control Management maintains a strong and consistent control environment through a joint accountability model that aligns managers with each function and region to mitigate operational risk. The team focuses on four areas: Control Design \& Expertise, Risks \& Controls Identification/Assessment, Issues \& Control Deficiencies and Control Governance \& Reporting.
Salary Context
This $118K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 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 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 $214,900 based on 6,420 positions with disclosed compensation. This role's midpoint ($154K) sits 28% below the category median. Disclosed range: $118K to $190K.
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
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.