Senior ML Engineer — LLMs & Production Systems

JPMorgan Chase & Co.

Palo Alto (CA)

On-site

USD 180,000 - 240,000

Full time

14 days+

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Job summary

JPMorgan Chase & Co. is seeking a Senior Machine Learning Engineer-Digital Intelligence in the Digital Intelligence team.

You will specialize in large language modeling, optimization, interpretability, and related algorithms, driving end-to-end ML solutions in a fast-paced environment. Ideal candidates bring a strong software engineering foundation with zero-to-one ML development experience and broad expertise in post-training models for complex data including tabular, temporal, and graphical

Qualifications

  • PhD with 2+ years or MS with 4+ years in CS, ML/LLM
  • Deep LLM and Transformer expertise
  • PyTorch at scale with distributed training
  • Foundation model pre-training experience
  • Strong software engineering for production systems
  • 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 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 teams to design solutions for product use cases

Skills

LLM/NLP
Transformer
PyTorch
Distributed training
Memory optimization
Production systems
Financial data

Education

PhD or Master’s in Computer Science

Tools

PyTorch

Job description

JPMorgan Chase & Co. is seeking a Senior Machine Learning Engineer-Digital Intelligence in the Digital Intelligence team.

You will specialize in large language modeling, optimization, interpretability, and related algorithms, driving end-to-end ML solutions in a fast-paced environment. Ideal candidates bring a strong software engineering foundation with zero-to-one ML development experience and broad expertise in post-training models for complex data including tabular, temporal, and graphical

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