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

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