LLM Researcher — Frontier Models & Systems

Wehrtyou; Greater London; On-site; GBP 149,000 - 223,000; Full time

Benefits offered by this job

Discretionary bonuses

Competitive benefits

Job summary

Hudson River Trading seeks an LLM-focused AI Researcher to join the HAIL team, developing and maintaining cutting-edge large language models used across trading workflows. You will work across tokenizer training, data curation, pretraining, post-training, evaluation, and live trading, with independence and minimal bureaucracy.

The role emphasizes frontier capability gains, with strong emphasis on engineering and collaboration with hardware and systems teams to realize impactful research in a

Qualifications

• Hands-on experience training, post-training, or evaluating LLMs at scale.
• Direct experience with large-scale pretraining, post-training (SFT, RL), LLM evaluation design, or distributed training/inference systems.
• Strong research sense to identify next steps and design ablations.
• Solid engineering skills in GPU kernels, PyTorch internals, or frontier-style LLM work.

Responsibilities

• Work on tokenizer training, dataset curation and mixing, pretraining, post-training, evaluation design, inference, and live trading.
• Contribute to frontier capability gains and ship code enabling breakthroughs.
• Collaborate with a small, focused team with minimal bureaucracy and with supporting engineering, hardware, and systems teams.

Skills

LLM training at scale

LLM evaluation design

Statistical analysis

Research taste

Engineering fundamentals

Tools

PyTorch internals

Distributed training

GPU kernels

Job description

Hudson River Trading seeks an LLM-focused AI Researcher to join the HAIL team, developing and maintaining cutting-edge large language models used across trading workflows. You will work across tokenizer training, data curation, pretraining, post-training, evaluation, and live trading, with independence and minimal bureaucracy.

The role emphasizes frontier capability gains, with strong emphasis on engineering and collaboration with hardware and systems teams to realize impactful research in a