Description
As part of a long-term research agenda within a newly formed systematic equities pod, we are building a proprietary Transformer-based model trained on tokenized intraday market data for next-token prediction of price movements. 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. This is a long-term research project with significant computational resources.
Principal Responsibilities:
- 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
Required Skills / Qualifications:
- 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 fine-tuning 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 tokenization 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
Millennium offers a total compensation package which includes a base salary, discretionary performance bonus, and comprehensive benefits. The estimated base salary range for this position is $150,000 to $200,000, which is specific to New York and may change in the future.