We are building a Machine Learning Research group to work directly with trading teams across the firm. Researchers are embedded into trading pods on a project basis, rotating across teams to solve high-impact problems at scale. This is a research-heavy role with real production ownership in a world-class, high-performance environment.
What You’ll Do
- Conduct applied ML research and deploy models into large-scale, low-latency production systems
- Work closely with traders, engineers, and researchers inside individual trading pods
- Design, train, and optimize deep learning models (including transformers and LLM-style architectures) for real-time decision-making
- Own projects end-to-end: research → experimentation → production deployment → iteration
- Contribute domain expertise (e.g. deep learning, LLMs, GPU optimization) while collaborating with other specialists on the team
Core Research Focus Areas
We are hiring across multiple, complementary profiles:
- Deep Learning / Representation Learning
- LLMs & Transformer Architectures
- GPU Optimization & High-Performance Training/Inference
- Statistical & Mathematical Modeling for Markets
Requirements
- PhD in Machine Learning, Computer Science, Statistics, Mathematics, Physics, or related field
- Strong research track record (e.g. Google Scholar, top-tier publications, or equivalent applied research impact)
- Proven experience building models and deploying them at scale in production
- Deep expertise in modern deep learning frameworks and model architectures
- Strong statistical and mathematical foundation
- Background from top-tier trading firms or leading big tech / research labs (e.g. Google/DeepMind, Meta, Apple, Tesla, etc.)
- Comfortable working in a high-performance, fast-iteration environment where scale and rigor matter
Nice to Have
- Prior experience in HFT, systematic trading, or market microstructure
- Experience optimizing models for GPU/accelerated hardware in production
- Exposure to real-time or low-latency systems
