Machine Learning Engineer, Digital Intelligence

What you'll do

• 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 that is reliable, secure, and scalable.
• Optimize training throughput for large data sources using distributed training frameworks like FSDP and DeepSpeed.
• Perform post-training optimization, interpretability, and steering techniques for large language models.
• Improve data quality and feedback loops to reduce agent effort and shorten resolution times.
• Mentor engineers on machine learning best practices and translate research advances into deployable systems.

What we're looking for

• Master's degree with 2+ years or Bachelor's with 4+ years in Computer Science with experience in Machine Learning, LLM/NLP, or similar fields.
• Deep expertise in LLMs and Transformers, including attention mechanisms, positional encodings like RoPE, and multi-modal data inputs.
• Proficiency in PyTorch at scale using distributed training frameworks such as FSDP and DeepSpeed with memory optimization techniques.
• 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).
• Experience with post-training LLMs, network optimization, interpretability, serving infrastructure, or large-scale compute (preferred).