# ML Research Scientist -Deep Learning & Transformer Architectures

**Company**: Quant Strategies
**Location**: New York, NY
**Work arrangement**: onsite
**Experience**: senior
**Job type**: full-time
**Salary**: $150,000 to $200,000
**Category**: Engineering
**Industry**: Finance

**Apply**: https://mlp.eightfold.ai/careers/job/755956532395?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_37ed18a5-bb9

## 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.

## Skills

### Required
- Transformer architectures
- PyTorch
- Machine learning
- Deep learning
- Attention mechanisms
- Positional encodings
- Tokenization strategies
- Training dynamics
- Linear algebra
- Probability theory
- Optimization
- Information theory
- Model training
- Python
- C++

### Nice to have
- Deep learning to financial data
- Tokenization approaches
- Published research
- Market microstructure
- Intraday trading dynamics
- Model compression
- Quantization
- Inference optimization

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