LLM & Production ML Engineer for Scalable Finance

Aumni; Palo Alto (CA); On-site; USD 170,000 - 260,000; Full time

Benefits offered by this job

Health care coverage

Retirement savings plan

Tuition reimbursement

Mental health support

Equity incentives

Job summary

JPMorgan Chase is seeking a Machine Learning Engineer-Digital Intelligence to join the Consumer & Community Banking division in Palo Alto. You will specialize in large language models, optimization, interpretability, and related algorithms, operating where research meets engineering to deliver scalable, real-world products.

You will collaborate with software developers and deep learning experts to advance ML for financial data, improving model performance and reliability in production

Qualifications

• Master’s degree with 2+ years or Bachelor’s with 4+ years in Computer Science with ML/NLP focus.
• Deep LLM and Transformer expertise with attention mechanisms and multi-modal data handling.
• PyTorch at scale with distributed training frameworks and memory optimization.

Responsibilities

• Research and prototype next-gen 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 teams to design product use-case solutions.

Skills

LLM/NLP

Transformer

PyTorch

Production systems

Financial data

Recommendation systems

Education

Master’s in CS

Bachelor’s in CS

Tools

FSDP

DeepSpeed

Distributed training

Job description

JPMorgan Chase is seeking a Machine Learning Engineer-Digital Intelligence to join the Consumer & Community Banking division in Palo Alto. You will specialize in large language models, optimization, interpretability, and related algorithms, operating where research meets engineering to deliver scalable, real-world products.

You will collaborate with software developers and deep learning experts to advance ML for financial data, improving model performance and reliability in production