Machine Learning Researcher

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.
If you are interested in exploring this, reply directly and Goliath Partners will be in touch.

Work arrangement
Yes