A leading global quantitative trading firm is hiring a Machine Learning Researcher into its central AI group in New York, adapting frontier-scale language models to one of the richest proprietary datasets in finance. The group's remit is simple to state and hard to do: take the best open-weight models in the world and make them genuinely expert at quantitative reasoning over petabyte-scale text and tabular data, on one of the largest private accelerator estates in the industry.
What you'll do:
- Post-train and domain-adapt state-of-the-art large language models on proprietary financial text and tabular data at petabyte scale.
- Own alignment and fine-tuning end to end: SFT, DPO, RLHF and parameter-efficient methods (LoRA/PEFT), plus the training infrastructure behind them.
- Build rigorous evaluation pipelines for reasoning, quantitative accuracy and strict factuality, in a setting where model outputs inform real decisions within days.
- Work at the metal when it matters: GPU memory management, mixed precision (FP16/BF16), quantisation and parallelisation strategies across a very large cluster.
- Collaborate closely with a small group of researchers and a dedicated research platform team, with a compute-to-researcher ratio few laboratories anywhere can match.
Your profile:
- Hands-on experience post-training large language models at a research lab, big tech group or applied AI team shipping real systems.
- Deep understanding of transformer architectures and modern alignment techniques.
- Strong Python with PyTorch or JAX, and the low-level GPU fluency to exploit large clusters efficiently.
- A track record of rigorous evaluation: you can prove a model got better, not just feel it.
- PhD or equivalent research depth preferred; publications welcome, shipped results count just as much.
- No finance background required. Genuine curiosity about applying frontier ML to a new domain is.
Why this role:
Frontier-lab-scale compute without frontier-lab queue politics, private data no model has ever been trained on, and a feedback loop measured by the real world in days rather than by benchmark leaderboards. The work is pure modern ML - post-training, alignment, evaluation - applied where it compounds fastest, with compensation at the very top of the quantitative industry.
Pre-Application:
- This is a full-time, on-site role based in New York; fully remote candidates will not be considered.
- Applicants must have the right to live and work in the US, or be eligible for sponsorship (confirmed case by case).
- Please ensure you meet the required experience prior to applying.
- Allow 1-5 working days for a response to any job enquiry.
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