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# ML Research Scientist -Deep Learning & Transformer Architectures

- **Company:** MILLENNIUM
- **Location:** New York, NY, United States
- **Salary:** $150,000.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** C++ (Programming Language), Encodings, Computer Programming, Cursor (Graphical User Interface Elements), Programming Tools, Information Theory, Python (Programming Language), Machine Learning, Tokenization, Pytorch, Large Language Models, Deep Learning, Information Technology
- **Published:** June 10, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=4bda2febb3d42220

## About the Role

Do you have experience in Statistics?, This is a long-term research project with significant computational resources. The successful candidate will have a PhD in machine learning or a related field and demonstrated ability to implement Transformer architectures from first principles., * PhD in Machine Learning, Computer Science, Statistics, Applied Mathematics, or a related field with a focus on deep learning

 * Demonstrated ability to implement Transformer architectures from scratch (not just finetuning pre-trained models)
 * Deep understanding of attention mechanisms, positional encodings, tokenization strategies, and training dynamics
 * Expert-level PyTorch skills including custom modules, training loops, mixed-precision, and multi-GPU training
 * Strong mathematical foundations: linear algebra, probability theory, optimization, information theory
 * Experience training models at scale (100M+ parameters)
 * Strong programming skills in Python and C++ for performance-critical components
 * Self-directed researcher capable of defining and executing a multi-month research agenda
 * Familiarity with Al-assisted development tools (Cursor, Claude Code)

Preferred Skills / Experience

 * Experience applying deep learning to financial data or time-series forecasting
 * Familiarity with tokenizatlon approaches for continuous or non-text data
 * Published research in top ML venues (NeurlPS, ICML, ICLR) or equivalent industry experience
 * Knowledge of market microstructure and intraday trading dynamics
 * Experience with model compression, quantization, and inference optimization

## Description

We are seeking an exceptional ML research scientist with deep expertise in Transformer architectures and large-scale model training. You will design, implement, and train a custom decoder-only Transformer from scratch -not fine-tune an existing LLM, but build a purpose* built architecture for financial time-series., * Design and implement a custom decoder-only Transformer architecture optimized for tokenized financial time-series data

 * Develop a novel tokenization scheme for intraday market data: price movements, volume, order flow, and cross-sectional features
 * Implement efficient training pipelines using PyTorch with mixed-precision training, gradient checkpointing, and multi-GPU parallelism
 * Design attention mechanisms adapted to financial data: temporal attention patterns, cross-asset attention, and multi-scale representations
 * Build evaluation frameworks for next-token prediction accuracy, signal quality, and trading performance
 * Implement inference optimization for low-latency production deployment: model quantization, KV-cache, speculative decoding
 * Conduct rigorous ablation studies to validate architecture choices and training methodology
 * Collaborate with the team to integrate model predictions into the live trading pipeline
 * Document research methodology, experimental results, and architectural decisions


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