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
