JP Morgan Chase

Runs a consumer bank and an investment bank

Applied AI and Machine Learning Lead

Natural Language Processing & LLMsConfirmed live in the last 24 hours

Full-time$171k - $260kIn-person

About the Job

Requirements

A master’s degree in computer science, engineering, statistics, or a related quantitative discipline is required.
At least 7 years of applied experience building and deploying machine learning and generative AI solutions in production environments is required.
Strong proficiency in Python and modern machine learning frameworks, such as PyTorch, is required.
The candidate must be able to lead experimentation and evaluation, including defining metrics, running controlled comparisons, and documenting results for stakeholders.
Experience deploying and operating models in production, including performance optimization, monitoring, and reliability trade-offs, is required.
Working knowledge of responsible AI practices, model risk concepts, and governance controls suitable for regulated environments is required.
Strong software engineering fundamentals, including version control, code review, testing, and maintainable design, are required.
The candidate must be able to partner across product, engineering, and business stakeholders to translate ambiguous needs into clear outcomes and delivery plans.

Responsibilities

Lead generative AI and agentic AI initiatives end to end, from problem framing and experimentation through production release and measurable adoption.
Design and evaluate foundation-model approaches, including model selection, fine-tuning strategies, retrieval and context design, and safety controls.
Establish evaluation practices for quality, robustness, latency, cost, and risk, and translate results into clear recommendations and roadmaps.
Build and operationalize production-grade machine learning and generative AI services using sound engineering practices, monitoring, and incident-ready controls.
Define and drive semantic consistency across systems by leading semantic modeling standards and lifecycle governance in partnership with domain experts.
Implement integration patterns that align data pipelines, application interfaces, and user experiences to reduce semantic conflicts and increase trust in outcomes.
Partner with cross-functional leaders to define success metrics, manage trade-offs, and deliver outcomes aligned to business priorities and oversight expectations.
Mentor and develop engineers and data scientists through technical leadership, reviews, and coaching to raise team capability and delivery velocity.
Communicate complex technical concepts to technical and non-technical audiences to influence decisions through clarity, evidence, and pragmatism.

Desired Qualifications

A PhD in a relevant quantitative field or demonstrated applied research impact in machine learning or generative AI is preferred.
Publications, patents, or open-source contributions demonstrating research depth and practical impact are preferred.
Hands-on experience with transformer-based architectures, large-scale training or fine-tuning, and GPU-enabled development workflows is preferred.
Experience building agentic AI systems involving tool use, orchestration, planning, and workflow automation with strong evaluation discipline is preferred.
Familiarity with semantic modeling, ontologies, or semantic-layer design to improve consistency across analytics and AI use cases is preferred.
Experience applying AI solutions within financial services or other highly regulated industries is preferred.

As an Applied AI and Machine Learning Lead at JPMorganChase within the Data Science and AI team supporting our Technology organization, you will drive high-impact generative AI and advanced analytics initiatives from research through production. You will lead the design, evaluation, and deployment of foundation-model and agentic AI capabilities that improve decision-making, automate work, and raise engineering standards for reliability and governance. You will partner with product, engineering, and risk stakeholders to define measurable outcomes and deliver solutions that are scalable, robust, and responsibly deployed.

Job Responsibilities

Lead end-to-end generative AI and agentic AI initiatives from problem framing and experimentation through production release and measurable adoption.
Design and evaluate foundation-model approaches, including model selection, fine-tuning strategies, retrieval and context design, and safety controls.
Establish rigorous evaluation practices (quality, robustness, latency, cost, and risk) and translate results into clear recommendations and roadmaps.
Build and operationalize production-grade machine learning and generative AI services using sound engineering practices, monitoring, and incident-ready controls.
Define and drive semantic consistency across systems by leading semantic modeling standards and lifecycle governance in partnership with domain experts.
Implement integration patterns that align data pipelines, application interfaces, and user experiences to reduce semantic conflicts and increase trust in outcomes.
Partner with cross-functional leaders to define success metrics, manage trade-offs, and deliver outcomes aligned to business priorities and oversight expectations.
Mentor and develop engineers and data scientists through technical leadership, reviews, and coaching to raise team capability and delivery velocity.
Communicate complex technical concepts to both technical and non-technical audiences, influencing decisions through clarity, evidence, and pragmatism.

Required Qualifications, Capabilities, and Skills

Master’s degree in computer science, engineering, statistics, or a related quantitative discipline.
7+ years of applied experience building and deploying machine learning and generative AI solutions in production environments.
Strong proficiency in Python and modern machine learning frameworks (for example, PyTorch).
Demonstrated ability to lead experimentation and evaluation, including defining metrics, running controlled comparisons, and documenting results for stakeholders.
Experience deploying and operating models in production, including performance optimization, monitoring, and reliability trade-offs.
Working knowledge of responsible AI practices, model risk concepts, and governance controls suitable for regulated environments.
Strong software engineering fundamentals, including version control, code review, testing, and maintainable design.
Proven ability to partner across product, engineering, and business stakeholders to translate ambiguous needs into clear outcomes and delivery plans.

Preferred Qualifications, Capabilities, and Skills

PhD in a relevant quantitative field or demonstrated applied research impact in machine learning or generative AI.
Publications, patents, or open-source contributions that demonstrate research depth and practical impact.
Hands-on experience with transformer-based architectures, large-scale training or fine-tuning, and GPU-enabled development workflows.
Experience building agentic AI systems (tool use, orchestration, planning, and workflow automation) with strong evaluation discipline.
Familiarity with semantic modeling, ontologies, or semantic-layer design to improve consistency across analytics and AI use cases.
Experience applying AI solutions within financial services or other highly regulated industries.

Base Pay/Salary: Jersey City,NJ $171,000.00-$260,000.00

About the Company

JP Morgan Chase

Runs a consumer bank and an investment bank

JPMorgan Chase runs the largest bank in the United States, which serves households and small businesses as Chase and large companies, governments, and investors as J.P. Morgan. During the 2008 financial crisis it bought the failing investment bank Bear Stearns and lender Washington Mutual, and in 2023 it took over First Republic Bank after regulators seized it. Chase takes deposits and earns interest on credit cards, mortgages, and car loans, while the investment side earns fees for advising on mergers, raising money for companies, trading stocks and bonds, and managing wealth. Formed in a 2000 merger, JPMorgan Chase traces its roots to 1799 and is based in New York City.

Company size: 10,001+
Company stage: IPO
Headquarters: New York City, New York
Founded: 1799

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