Applied Machine Learning Scientist - Vice President

JPMorgan Chase & Co. • Palo Alto, CA

1d • $171,000 - $260,000

About The Position

As an Applied Machine Learning Scientist at the Vice President level, you will lead the development of scalable, production-grade machine learning solutions in areas including natural language processing, speech recognition, recommendation systems, information retrieval, and agentic AI. You will design and productionize generative AI systems, including LLM-powered retrieval-augmented generation, tool- and function-calling agents, and structured generation to automate complex workflows and improve customer experiences.

You will work with product, engineering, control, and other partner teams to turn ambiguous problems into measurable goals, deliver robust models, and operate them reliably in production. The role calls for deep learning and transformer-modeling expertise, practical experience fine-tuning and evaluating models, strong analytical thinking, motivation to learn, and contributions to a knowledge-sharing culture. The position is part of JPMorganChase’s Consumer & Community Banking Data & Analytics team, which uses data responsibly to support business outcomes and customer protection.

Requirements

• MS degree with 7 or more years, or PhD with 4 or more years, of hands-on industry experience building and deploying production machine learning systems in NLP, information retrieval, recommendation systems, and/or generative AI.
• Good understanding of current NLP concepts, including transformer architecture, knowledge distillation, transfer learning, and representation learning.
• Applied generative AI experience with LLMs and ability to fine-tune and deploy SLMs for targeted use cases; familiarity with prompt design, grounded generation, and RAG.
• Experience scaling LLM systems, including caching, batching, prompt/version governance, and evaluation harnesses.
• Strong foundation in machine learning, deep learning, and statistical modeling, including model evaluation and error analysis.
• Solid understanding of information retrieval concepts such as indexing, ranking, dense and sparse retrieval, and reranking, and/or recommendation systems.
• Ability to design experiments, establish strong baselines, select meaningful metrics, and rigorously evaluate model performance.
• Scientific thinking, inventiveness, and ability to work independently and collaboratively.
• Proficiency in Python and common machine learning libraries, including PyTorch or TensorFlow, Hugging Face, and scikit-learn, with ability to write production-quality code.
• Ability to collaborate across product, engineering, and control functions.
• Solid written and spoken communication skills.

Nice To Haves

• Five years of hands-on experience developing and optimizing virtual assistant models.
• Experience orchestrating multi-agent teams with supervisor agents, debate or consensus mechanisms, and role-specialized toolkits for complex enterprise tasks.
• Experience building agent governance and evaluation suites, including red-teaming, adversarial tests, safety scorecards, and prompt/tool regression suites.
• Experience with reinforcement learning or bandits, preference optimization, or human feedback loops for personalization.
• Experience in regulated finance domains and with risk/control processes.
• Experience with MLOps/LLMOps, including model CI/CD, monitoring and alerting, model versioning, pipeline evaluation, and rollback strategies.
• Experience with A/B experimentation and data- and metric-driven product development.

Responsibilities

• Lead and deploy 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 approaches, including policy optimization, bandits, and RLHF-style methods where appropriate, to improve personalization, dialog policies, and sequential decision-making.
• Fine-tune and adapt LLMs and SLMs using PEFT methods such as LoRA, AdaLoRA, and IA3, as well as distillation and quantization, optimizing quality, latency, cost, and production constraints.
• Select and innovate on machine learning strategies for banking problems.
• Analyze and evaluate the ongoing performance of developed machine learning systems.
• Collaborate with Business, Technology, Product Management, Design, Analytics, Model Governance, and other partner teams to deploy solutions into production.
• Build domain understanding to identify high-impact opportunities, ensure responsible AI usage, and drive measurable customer experience, automation, accuracy, and efficiency outcomes.
• Implement privacy, safety, and security controls for generative AI systems, including PCI handling and redaction, policy checks, jailbreak resistance, and auditability.

Benefits

• Competitive total rewards package, including base salary determined by role, experience, skill set, and location.
• Eligible roles may receive commission-based pay and/or discretionary incentive compensation in cash and/or forfeitable equity.
• Comprehensive health care coverage.
• On-site health and wellness centers.
• Retirement savings plan.
• Backup childcare.
• Tuition reimbursement.
• Mental health support.
• Financial coaching.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior