Machine Learning Researcher - Large Language Models

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.

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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.

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• 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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