Machine Learning Engineer – Digital Intelligence

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 - strongcommand of attention mechanisms, positional encodings such as RoPE, and the ability to handle multi-modal data inputs effectively.

-PyTorchproficiency 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 atscale.

-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.”