Machine Learning Engineer - Digital Intelligence

JPMorganChase; Palo Alto (CA); On-site; USD 130,000 - 210,000; Full time

Job summary

JPMorganChase in Palo Alto is seeking a Machine Learning Engineer specializing in digital intelligence to advance large language model systems. You will translate research into scalable production code and collaborate with a team of engineers and ML experts.

The role emphasizes building reliable, secure AI services, optimizing training, handling multi-modal data, and applying rigorous metrics to improve performance in real-world banking applications.

Qualifications

• 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, RoPE, and multi-modal data inputs.
• PyTorch proficiency at scale — distributed training with FSDP/DeepSpeed, memory optimization.
• Foundation model training — pre-training from scratch and token/vocabulary design for heterogeneous data (tabular, temporal, graphical).
• Strong software engineering skills; production-quality systems that scale.

Responsibilities

• Research and prototype architectures for structured and unstructured data.
• Develop pre-training objectives for financial event sequences and heterogeneous profile data.
• Implement research ideas in production-quality code.
• Mentor engineers on ML best practices and deployable systems.
• Optimize training throughput for large data sources.
• Collaborate with other teams to design product-use solutions.

Skills

LLM/NLP

Transformer architectures

Distributed training

Post-training LLMs

Memory optimization

Multi-modal data handling

Education

Master's degree in Computer Science

Bachelor's degree in Computer Science

Tools

PyTorch

DeepSpeed

FSDP

Job description

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.