Senior Machine Learning Engineer - Digital Intelligence

JPMorganChase; Palo Alto (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

JPMorgan Chase in Palo Alto seeks an experienced Senior Machine Learning Engineer in Digital Intelligence to advance LLM-based solutions with a strong software engineering foundation. You will collaborate with deep learning experts to prototype and productionize models that handle financial event data and multi-modal inputs.

The role emphasizes research-to-production work, scalable training, and optimization of model performance in a fast-paced environment within the Digital Intelligence team.

Qualifications

• PhD with 2+ years OR Master’s with 4+ in CS with ML/LLM/NLP.
• Deep LLM and Transformer expertise; attention mechanisms and multi-modal data handling.
• PyTorch proficiency at scale with distributed training (FSDP, DeepSpeed).
• Experience with pre-training from scratch and token/vocabulary design for diverse data sources.
• Strong software engineering skills for robust, production-quality systems.
• Prior experience with financial data and recommendation systems.

Responsibilities

• Research and prototype next-generation architectures for structured and unstructured data.
• Develop pre-training objectives for financial event sequences and heterogeneous 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 solutions for product use cases.

Skills

LLM/NLP expertise

PyTorch at scale

Foundation model training

Software engineering

Financial data experience

Education

PhD in Computer Science

Master’s in Computer Science

Tools

PyTorch

FSDP

DeepSpeed

Job description

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 Senior Machine Learning Engineer-Digital intelligence in the Digital Intelligence team, you will be collaborating with a high-caliber team of software developers and deep learning experts, 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

• PhD with 2+ years OR Master's degree with 4+ 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.