Applied Machine Learning Scientist - Vice President

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.

• 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.

• 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

• 5 years of hands-on experience with virtual assistant model development and

• 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

• Scientific thinking with the ability to invent and to work both independently

• 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

• 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,

• 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.

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.