Applied Machine Learning Scientist - Vice President

JPMorganChase; Palo Alto (CA); Sur place; USD 180 000 - 240 000; Plein temps

Résumé du poste

JPMorganChase seeks an Applied Machine Learning Scientist to lead production‑grade ML solutions across NLP, speech, and GenAI. You will design and deploy LLM‑powered systems, including RAG, tool calls, and multi‑agent workflows, with focus on reliability and measurable customer impact.

Collaborate with product, engineering, and governance teams to translate ambiguous problems into concrete goals, fine‑tune models, evaluate performance, and ensure privacy, safety, and security controls in banking

Qualifications

• MS with 7+ years, or PhD with 4+ years of hands-on industry experience in building and deploying ML systems (NLP/IR/Rec) in production.
• Strong understanding of transformer architectures, knowledge distillation, transfer learning, and representation learning.
• GenAI experience with LLMs, fine-tuning and deployment of SLMs for targeted use cases; prompt design and grounded generation.
• Experience with scaling LLM systems (caching, batching, governance, evaluation harnesses).
• Solid foundation in ML, deep learning, and statistical modeling, including evaluation and error analysis.
• Understanding of IR concepts (indexing, retrieval, dense/sparse, re-ranking) and/or recommender systems.
• Ability to design experiments with baselines, meaningful metrics, and rigorous evaluation.
• Scientific thinking with ability to work independently and in collaborative teams.
• Proficiency in Python and ML libraries; production-quality code.
• Ability to collaborate across product, engineering, and governance teams.

Responsabilités

• Lead and deploy state-of-the-art ML systems across NLP, speech, recommendations, and information retrieval.
• Design and build agentic AI systems for multi-step workflows with tool calling, multi-agent orchestration, planning, grounding, and safety guardrails.
• Apply RL methods (policy optimization, bandits, RLHF-like) to improve personalization and dialog policies.
• Fine-tune and adapt LLMs/SLMs using PEFT, distillation, and quantization; optimize quality, latency, and cost.
• Select and innovate ML strategies for banking problems.
• Analyze ongoing system performance and iterate improvements.
• Collaborate with Business, Technology, Product, Design, Analytics, and Governance to deploy in production.
• Build domain understanding, ensure responsible AI usage, and drive measurable outcomes (customer experience, automation, accuracy, efficiency).
• Implement privacy, safety, and security controls for GenAI systems, including PCI handling and auditability.

Connaissances

NLP/IR/Rec Systems

GenAI experience

LLM fine-tuning

Python & ML libs

Model evaluation

Formation

MS or PhD in a relevant field

Outils

PyTorch

TensorFlow

Hugging Face

scikit-learn

Description du poste

Job Description

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.

As an Applied Machine Learning Scientist within our dynamic team, you will lead the development of scalable, production‑grade advanced ML solutions across natural language processing, speech recognition, recommendation systems, information retrieval, and agentic AI. You will play a key role in delivering Generative AI capabilities – designing and productionizing LLM‑powered systems such as RAG (Retrieval Augmented Generation), tool/function‑calling agents, and structured generation to automate complex workflows and improve customer experiences. You will collaborate with product, engineering, and control partners to translate ambiguous problems into measurable goals, deliver robust models, and operate them reliably in production. You bring strong deep learning and transformer‑based modeling expertise, as well as hands‑on experience in fine‑tuning and evaluation. You must have a strong passion for machine learning, strong analytical thinking, a deep desire to learn, and high motivation. You must also invest independent time in learning, researching, and experimenting with new innovations, and contribute to a strong knowledge‑sharing culture.

Job Responsibilities

• Lead and deploy state‑of‑the‑art advanced machine learning systems across NLP, speech recognition, recommendation systems, and information retrieval.
• Design and build agentic AI systems for multi‑step workflows, including tool/function calling, multi‑agent orchestration, planning, grounding, and safety guardrails.
• Use reinforcement learning (policy optimization, bandits, RLHF‑style approaches where appropriate) to improve personalization, dialog policies, and sequential decision‑making systems.
• Fine‑tune and adapt LLMs/SLMs using PEFT (LoRA, AdaLoRA, IA3), distillation, and quantization; optimize for quality, latency, cost, and production constraints.
• Select and innovate on ML strategies for various banking problems.
• Analyze and evaluate the ongoing performance of developed ML systems.
• Collaborate with multiple partner teams, such as Business, Technology, Product Management, Design, Analytics, and Model Governance to deploy solutions into production.
• Build domain understanding to identify high‑impact opportunities, ensure responsible AI usage, and drive measurable outcomes (customer experience, automation, accuracy, and efficiency).
• Implement privacy, safety, and security controls for GenAI systems, including PCI handling/redaction, policy checks, jailbreak resistance, and auditability.

Required Qualifications, Capabilities, And Skills

• MS with 7+ years, or PhD with 4+ years of hand‑on industry experience in building and deploying machine learning systems (NLP/Information Retrieval/Recommendation System and/or GenAI) in production environment
• Good understanding of the latest advancement of NLP concepts, such as the transformer architecture, knowledge distillation, transfer learning, and representation learning.
• Applied GenAI experience with LLMs and the ability to fine‑tune and deploy SLMs for targeted use cases, familiarity with prompt design, grounded generation, and RAG.
• Experience with scaling LLM systems (caching, batching, prompt/version governance, evaluation harnesses)
• Strong foundation in machine learning, deep learning, and statistical modelling, including model evaluation and error analysis.
• Solid understanding of Information Retrieval concepts (indexing, ranking, dense/sparse retrieval, re‑ranking) and/or recommendation systems.
• Ability to design experiments — establish strong baselines, choose meaningful metrics, and evaluate model performance rigorously
• Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments
• Proficiency in Python and common ML libraries (PyTorch/TensorFlow, Hugging Face, scikit‑learn), and ability to write production‑quality code.
• Ability to collaborate in cross‑functional environments with product, engineering, and control partners.
• Solid written and spoken communication skills

Preferred Qualifications, Capabilities, And Skills

• 5 years of hands‑on experience with virtual assistant model development and optimization
• Experience orchestrating multi‑agent teams with supervisor agents, debate/consensus mechanisms, and role‑specialized toolkits for complex enterprise tasks.
• Building agent governance and eval suites: red‑teaming, adversarial tests, safety scorecards, regression suites for prompts/tools
• Experience with RL/bandits, preference optimization, or human feedback loops for personalization.
• Experience in regulated finance domains and working with risk/control processes.
• Experience with MLOps/LLMOps: CI/CD for models, monitoring/alerting, model versioning, evaluation of pipelines, and rollback strategies.
• Experience with A/B experimentation and data/metric‑driven product development.

This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorganChase's review of criminal conviction history, including pretrial diversions or program entries.

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 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.

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.

The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.