Applied Machine Learning Scientist – Vice President

Archer; Palo Alto (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Archer, in collaboration with JP Morgan Chase, seeks an experienced ML leader to architect and deploy end-to-end machine learning systems for NLP, information retrieval, and GenAI in production. You will design agentic AI workflows, implement RLHF-based improvements, and scale LLMs with PEFT and distillation.

collaboration with business, technology, and governance teams is essential to deliver measurable outcomes.

Qualifications

• MS with 7 years of hand-on industry experience in building and deploying ML systems (NLP/IR/RecSys) in production
• PhD with 4 years of industry experience in ML systems is acceptable in lieu of MS
• Strong understanding of transformer architectures, distillation, transfer learning, and representation learning
• Experience with GenAI, LLMs, and fine-tuning for practical use cases (prompt design, grounded generation, RAG)
• Experience scaling LLM systems (caching, batching, governance, evaluation harnesses)
• Solid ML, DL, and statistical modeling foundations; model evaluation and error analysis
• Proficiency in Python and ML libraries; production-quality coding
• Ability to collaborate cross-functionally with product, engineering, and risk/compliance partners

Responsibilities

• Lead and deploy ML systems across NLP, IR, and GenAI production environments
• Design agentic AI systems for multi-step workflows with tool calling and multi-agent orchestration
• Apply RL/RLHF approaches to improve personalization and dialog policies
• Fine-tune and deploy SLMs/LLMs using PEFT, distillation, and quantization
• Set up experiments with robust baselines and meaningful metrics to evaluate models
• Collaborate with Business, Technology, Product, Design, Analytics and Governance teams
• Ensure responsible AI usage, privacy, safety, and security controls across GenAI systems
• Monitor and evaluate performance, latency, cost, and production constraints
• Deposit governance for supervision, safety scoring, and auditability

Skills

NLP/IR/RecSys in production

GenAI & LLM fine-tuning

Python & ML frameworks

Cross-functional collaboration

Experiment design & evaluation

Safety & governance

RL/RLHF concepts

Production-grade coding

Education

MS in a relevant field

PhD in a relevant field

Tools

PyTorch/TensorFlow

Hugging Face

scikit-learn

CUDA / GPUs

Job description

*Archer is collaborating with J.P. Morgan to connect them with exceptional professionals for this role. 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.

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 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.

Equal Opportunity Employer/Disability/Veterans

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.