Our client is a highly profitable, fast-growing quantitative trading firm operating at the exact intersection of frontier AI and elite market-making. Founded by veteran researchers and engineers from OpenAI, Anthropic, and Google DeepMind, alongside top-tier quantitative minds from Jump Trading, Citadel, and Jane Street, they are building from a completely clean-slate architecture. This is a rare opportunity to join a small, stealth team where machine learning isn't bolted onto the trading stack; it is the trading stack.

Financial markets are one of the hardest machine learning problems on earth: adversarial, non-stationary, noisy, and unforgiving. Every model you ship is tested against the smartest competitors in the world in real time, and the feedback loop is measured in PnL, not benchmarks. For researchers who want their work to matter the moment it's deployed, there are few environments like it.

What you'll do

• Push frontier ML research into live markets, applying the latest advances in deep learning, sequence modeling, and representation learning to one of the most data-rich prediction problems in existence
• Design, train, and scale models on massive volumes of high-frequency market data, from raw order-book dynamics to cross-asset signals
• Tackle open research problems that academia hasn't solved yet: learning under regime shifts, extracting signal from extreme noise, and building models that adapt as the market adapts to them
• Take ideas from whiteboard to production, working side by side with engineers who build the low-latency infrastructure your models run on
• Help define the research direction of a firm built to treat constant technological evolution as a structural edge rather than a liability

What you bring

• Deep expertise in machine learning, with a track record of research at a frontier AI lab, a top research group, or an elite quant firm
• Strong hands-on skills in Python and modern ML frameworks such as PyTorch or JAX, with experience training and scaling large models
• A rigorous mathematical and statistical foundation
• An instinct for turning research into systems that work in the real world, not just on paper

Nice to have

• A PhD in Machine Learning, Computer Science, Statistics, Mathematics, Physics, or a related field
• Publications at top venues such as NeurIPS, ICML, or ICLR
• Experience with time series, reinforcement learning, or large-scale distributed training
• Prior quant or trading experience is welcome but not required; exceptional researchers from AI labs and academia are highly encouraged to apply

Why this role

• Work alongside people who have already built frontier AI systems and world-class trading operations
• Real-world impact: your models go live and are judged by the market itself
• A clean-slate architecture with none of the legacy constraints of established firms
• A small, flat team where research ideas move to production fast

Compensation is highly aggressive, offering a $450k base plus significant equity.

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