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About This Role
JOB DESCRIPTION
The Controls Room is a physical and virtual 'room' containing firmwide control\-related information to enable rapid access to relevant data, advanced analytics, and proactive issue identification. The Controls Room Team is designed to facilitate better monitoring and management of the Firm's control environment through the creation and maintenance of Firmwide Systems which support key Operational Risk Frameworks as well as a Firmwide Reporting Utility that provides standardized control\-related data and encourages quick, efficient and accurate reporting and analytics. The primary goal is to enhance risk and control oversight through the standardization and automation of operational risk recording and reporting as well as provide access to firmwide aggregated information and to produce business risk insight.
Description for Internal Candidates
Central Control Management solidifies an effective firmwide control framework within and across all lines of business by identifying and remediating control issues with a sense of urgency, irrespective of the functional area. The Central Control Management team works collaboratively with other control disciplines and oversees existing control functions as well as the development of new control functions and protocols. This process enables the firm to engage the appropriate teams in a timely manner and provides the ability to quickly remediate critical control issues across all the impacted areas of the firm.
Help build agentic systems that turn control data into actionable insight through multi\-step reasoning, dynamic tool use, and intelligent workflow orchestration. This role offers the opportunity to design and implement intelligent agents that understand control problems, retrieve relevant information, reason across complex scenarios, and take guided action. You will work across data, engineering, and business teams to develop agents that interact naturally with users and systems while maintaining appropriate governance and control. Your work will contribute directly to faster decision\-making, smarter automation, and improved risk oversight.
Job summary
As a Controls Room Quant Modeling Senior Associate in the Controls Room team, you design and build agentic systems that improve control monitoring, analytics, and decision support. You will work on agent architecture, workflow orchestration, tool integration, and agent reasoning patterns that enable natural, multi\-step problem solving. We are looking for someone with strong engineering fundamentals, practical experience with LLMs and agent frameworks, and the ability to contribute effectively across data, engineering, and business teams.
Job responsibilities
- Design and implement Multi\-Agent Dynamic System hat enable multi\-turn reasoning, tool use, and dynamic decision\-making for control management problems
- Develop and Design and integrate tools and data connections that agents use to retrieve information, validate data, and take action
- Build and maintain orchestration logic for agent routing, state management, and workflow coordination
- Work with LLMs and prompt design to shape agent behavior, reasoning patterns, and response quality
- Contribute to agent architecture and design decisions, including agent decomposition, memory management, and multi\-agent coordination
- Partner with data, product, and engineering teams to translate business problems into practical agentic solutions
- Implement validation, testing, and evaluation frameworks for agent performance, accuracy, and reliability
- Support observability and monitoring for agent systems, including reasoning traces, tool calls, and decision auditing
- Help build reusable agent templates, patterns, and tooling that accelerate delivery across use cases
- Document agent design, assumptions, tool specifications, and operational procedures for technical and non\-technical audiences
- Support governance and control requirements by implementing traceability, human\-in\-the\-loop checkpoints, and appropriate safeguards into agent workflows
Required qualifications, capabilities, and skills
- Bachelor's degree, Master's degree, or Ph.D. in Computer Science, Data Science, Engineering, or a related quantitative discipline
- 3\+ years of experience building software, data, or backend systems in production or near\-production environments
- Strong Python programming skills, including debugging, testing, and writing maintainable code
- Foundational understanding of Large Language Models, transformer\-based architectures, and LLM capabilities and limitations
- Experience with at least one LLM\-based agentic framework or library
- Experience integrating external tools, APIs, and data services into software or machine learning systems
- Experience designing or implementing multi\-step workflows, state machines, or orchestration logic
- Experience with prompt engineering, prompt templates, and shaping model behavior through instructions
- Familiarity with monitoring, logging, and operational support practices for production applications
- Ability to solve problems independently within defined scope and collaborate effectively across cross\-functional teams
- Ability to communicate technical work clearly to both technical and non\-technical stakeholders
Preferred qualifications, capabilities, and skills
- Experience building or deploying agentic systems in production or enterprise environments
- Experience with retrieval\-augmented generation (RAG) and grounding strategies for LLMs
- Experience with async task execution, event\-driven workflows, or asynchronous coordination patterns
- Experience implementing agent memory, context management, or multi\-turn conversation patterns
- Familiarity with responsible AI practices, model governance, or human\-in\-the\-loop review patterns in agent systems
- Experience with observability and tracing tools for understanding system behavior and reasoning
- Experience in risk, controls, compliance, or other highly governed environments
Please note\- We are unable to provide sponsorship for this role now or in the future.
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 $114K-$170K 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($142K) sits 35% below the category median. Disclosed range: $114K to $170K.
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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