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. In this role, you’ll apply strong technical judgment to choose the right approaches (including modern LLM-based methods where appropriate), evaluate performance with rigorous metrics, and ensure solutions are reliable, secure, and scalable in real-world environments. You’ll also contribute to improving data quality and feedback loops, monitoring models in production, and continuously iterating to reduce agent effort, shorten resolution times, and increase consistency and quality across operational workflows.
As a Machine Learning Engineer-Digital intelligence in the Consumer & Community Banking division, you will be collaborating with a high-caliber team of software developers and deep learning experts, and you will specialize in large language modeling, optimization, interpretability, and related algorithms.
The ideal candidate brings a strong software engineering foundation combined with hands-on, zero-to-one machine learning development experience. You will possess broad expertise in post-training machine learning models — including quality and performance optimization — alongside deep knowledge of large language models and modern deep learning techniques. Above all, you will have a demonstrated ability to operate at the intersection of research and engineering, turning promising ideas into scalable, real-world products within a fast-paced, collaborative environment.
Job Responsibilities
Research and prototype next-generation architectures for structured and unstructured data
Develop novel pre-training objectives tailored to financial event sequences and heterogeneous profile data
Implement research ideas in production-quality code
Mentor engineers on ML best practices; translate research advances into deployable systems
Optimize training throughput for large data sources
Collaborate with other teams to design solutions for product use cases.
Required qualifications, capabilities, and skills:
- Master’s degree with 2+ years Or Bachelor's with 4+ years in Computer Science, with training and work experience in Machine Learning, LLM/NLP or similar fields.
-Deep LLM and Transformer expertise — strong command of attention mechanisms, positional encodings such as RoPE, and the ability to handle multi-modal data inputs effectively.
-PyTorch proficiency at scale — hands-on experience with distributed training frameworks including FSDP and DeepSpeed, alongside practical memory optimization techniques.
-Foundation model training — proven experience in pre-training from scratch and designing tokens and vocabularies for complex, heterogeneous data sources including tabular, temporal, and graphical formats.
-Strong software engineering skills — ability to build robust, production-quality systems that perform reliably at scale.
-Prior experience with financial data and recommendation systems.
Preferred qualifications, capabilities, and skills:
Publication record at top AI/ML venues.
Experience optimizing serving infrastructure is a plus.
Experience with post-training LLMs and network optimization algorithms, as well as interpretability or steering techniques for LLMs.
Experience working with large-scale compute infrastructure.
Experience shipping a real-world product, project, or feature.
Experimental rigor and ablation design when benchmarking LLM optimizations.
Strong communication and accountability skills, with a collaborative mindset and strong work ethic.
“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.
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 TeamJPMorganChase San Francisco, California, USA Office
San Francisco, CA, United States
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