below marketmost similar roles pay hereabove market
This listing doesn't post a salary. Most similar roles pay
$201,250–$254,750.
Based on 240 similar postings.
Employer
About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has
1226 open roles
on FindRole.
Listed pay typically runs
$156,750–$215,000
across 5 roles with salary data.
As a Machine Learning Engineer – Digital Intelligence within the Consumer & Community Banking division, you will join a team of software developers and deep learning experts specializing in large language modeling, optimization, and interpretability. You will research and prototype next-generation architectures for structured and unstructured data, develop novel pre-training objectives for financial event sequences, and implement research ideas into production-quality code. Your work involves optimizing training throughput, improving data quality, and ensuring solutions are reliable, secure, and scalable. You must possess deep expertise in Transformers, attention mechanisms, and RoPE, alongside PyTorch proficiency using FSDP and DeepSpeed. The role requires experience in foundation model training from scratch, handling multi-modal data, and building robust systems for financial data and recommendation systems to reduce agent effort and improve operational workflows.
What does a Machine Learning Engineer earn in California?
Median $235750 from 200 postings across 34 companies.
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 into production-quality code.
Optimize training throughput for large data sources using distributed training frameworks.
Monitor models in production to reduce agent effort and shorten resolution times.
Mentor engineers on ML best practices and translate research advances into deployable systems.
Design solutions for specific product use cases in collaboration with other teams.
What we're looking for
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 including attention mechanisms, positional encodings like RoPE, and multi-modal data inputs.
PyTorch proficiency at scale with hands-on experience in distributed training frameworks like FSDP and DeepSpeed.
Proven experience in foundation model pre-training from scratch and designing tokens/vocabularies for complex, heterogeneous data sources.
Strong software engineering skills to build robust, production-quality systems that perform reliably at scale.
Prior experience with financial data and recommendation systems.
Publication record at top AI/ML venues (preferred).