LLM Engineer for Digital Intelligence

Fairygodboss; Palo Alto (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

Chase is seeking a Machine Learning Engineer in Digital Intelligence to join a high-caliber team of software developers and deep learning experts. You will specialize in large language modeling, optimization, interpretability, and related algorithms.

The role emphasizes turning research into scalable, real-world products, with strong focus on reliability, security, and performance in production environments.

Qualifications

• Master's degree with 2+ years or Bachelor's with 4+ years in Computer Science, with ML/LLM/NLP experience.
• Deep LLM and Transformer expertise with attention mechanisms and RoPE.
• PyTorch at scale with distributed training (FSDP/DeepSpeed) and memory optimization.
• Foundation model training including pre-training and token/vocabulary design for heterogeneous data.
• Strong software engineering to deliver production-grade 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; translate research into 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

Transformer models

Distributed training

Software engineering

Financial data experience

Education

Master's degree in Computer Science

Bachelor's degree in Computer Science

Tools

FSDP

DeepSpeed

Job description

Chase is seeking a Machine Learning Engineer in Digital Intelligence to join a high-caliber team of software developers and deep learning experts. You will specialize in large language modeling, optimization, interpretability, and related algorithms.

The role emphasizes turning research into scalable, real-world products, with strong focus on reliability, security, and performance in production environments.