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About This Role
JOB DESCRIPTION
Job Description
As a Technical Program Manager at JPMorgan Chase, you will lead complex technology projects and programs that advance business goals and create value for clients, employees, and stakeholders. Leveraging broad technical expertise, analytical reasoning, and adaptability, you will develop innovative solutions and navigate ambiguity and change. Your strong communication skills will enable effective collaboration with cross\-functional teams and the management of stakeholder relationships, ensuring alignment on project objectives and governance. By optimizing resources, proactively managing risks, and driving strategic decision\-making, you will be instrumental in delivering high\-impact projects that shape the firm's future. Your self\-motivation and proactive approach will be essential for independently managing multiple priorities and ensuring programs align with business objectives and timelines, delivering impactful results in a fast\-paced environment.
Key Attributes for Success
- Self\-motivated and proactive, committed to continuous improvement and driving impactful results.
- Possesses a strong strategic vision, aligning project goals with broader business objectives and anticipating future opportunities and challenges.
- Thrives in ambiguity, bringing clarity to stakeholders and collaborating effectively both independently and within teams.
Responsibilities
- Independently manage technical programs, integrating AI strategy to achieve program goals and deadlines.
- Maintain project plans, track progress, report status, and escalate risks/issues as needed.
- Proactively manage Risks, Assumptions, Issues, and Dependencies (RAID) for assigned projects.
- Identify and implement opportunities to improve project management processes, leveraging AI\-enabled tools and sharing best practices.
- Coordinate with cross\-functional teams, product owners, and external partners to ensure effective project delivery and communicate strategy.
- Organize and facilitate project meetings, prepare agendas, capture notes, and follow up on action items.
- Track key metrics and outcomes, support continuous improvement, and maintain inventories of initiatives for gap analysis.
- Drive strategic program vision and coordinate priorities across various workstreams.
- Lead teams, stakeholders, and cross\-functional partners to achieve optimal business value.
- Use collaborative storytelling to drive cross\-functional alignment for program work.
- Drive measurable outcomes with clear, objective success metrics.
- Lead high\-stakes tech efforts with \<72\-hour turnaround—tight scope, clear decision rights, and fast execution with risk/rollback plans.
Required Qualifications, Capabilities, and Skills
- Demonstrated self\-motivation and commitment.
- Strategic thinking, strong problem\-solving abilities, and technical depth.
- Expertise in risk and change management, influencing, and negotiation.
- Results\-driven execution and adaptability in a fast\-paced environment.
- Strong organizational and project management skills; able to manage multiple priorities independently.
- Proficiency with GenAI tools (e.g., LLM Suite) and other firm\-approved AI tools.
- Excellent communication and interpersonal skills for stakeholder engagement.
- Analytical skills to track key performance metrics, monitor project health, and identify areas for improvement.
- Collaborative mindset; able to work across teams and with external partners.
- Proficiency in Office 365 project management tools (e.g., Teams, PowerPoint, Excel, SharePoint).
- Strong aptitude for rapidly acquiring new skills and knowledge in technology and AI.
- Analytical reasoning, problem\-solving, and the ability to break down objectives into tasks.
Preferred Qualifications, Capabilities, and Skills
- 7\+ years of product management, program management, or relevant experience.
- 5\+ years managing dynamic and fast\-paced portfolios of work.
- Familiarity with BI tools (e.g., Alteryx, Tableau) plus Python for automation, analysis, or tooling.
- Proficiency with developer tooling (e.g., Visual Studio Code (VS Code), GitHub Copilot) and familiarity with AI integration patterns and protocols such as MCP (Model Context Protocol).
- Working knowledge of one or more of the following areas:
+ Resiliency \& Capacity: reliability targets; plan availability and disaster recovery
+ Efficiency Governance: unit cost awareness, waste reduction, utilization guardrails.
+ Database Fundamentals: resiliency, backup/restore, upgrades, migrations.
+ Incident Leadership: coordinate response, comms, timelines, closeout actions.
+ Delivery Orchestration: end\-to\-end delivery lifecycle and impact.
+ Pipelines: CI/CD concepts, release workflows, and deployment controls.
+ Patterns \& IaC: reusable engineering patterns and infrastructure\-as\-code fundamentals
+ Jira Align: manage epics/features and roadmap/status reporting.
ABOUT US
Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.
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.
Equal Opportunity Employer/Disability/Veterans
ABOUT THE TEAM
Our Consumer \& Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most\-used digital solutions – all while ranking first in customer satisfaction.
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. Mid-level AI roles across all categories have a median of $200,000.
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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