AI Researcher, LLMs
A research role on the LLM team inside HRT's AI Labs (HAIL), which builds language models from scratch for use across the firm. The work spans the whole development loop up to live trading use.
What you’d work on
Our reading of the posting: what a new hire in this role would most likely spend their first months on.
- Train tokenizers and curate and mix datasets
- Run pretraining and post-training experiments
- Design evaluations and analyse them statistically
- Ship inference code for firm-wide use
What they ask for
Required
- Two or more years training, post-training or evaluating LLMs at scale
- Experience in pretraining, SFT or RL post-training, LLM evaluation or distributed systems
- Research judgement: choosing experiments and ablations
- Evidence-based updating of beliefs
Preferred
- GPU kernel development
- PyTorch internals
- Published frontier-style LLM work
- Education
- No degree level stated.
- Experience
- 2+ years working hands-on with LLMs at scale
- Languages and tools
How to prepare
- Pretrain a small language model and run ablations on data mix
- Study post-training methods such as SFT and RL
- Design an evaluation set and test whether differences are statistically significant
- Learn distributed training and efficient inference
What to study
- Financial Machine Learning
- Text as Data and NLP for Finance
- Python for Quantitative Finance
- Probability, Statistics and Financial Data
The NLP and ML Quant Researcher roadmap sets out the stages for this kind of role, with free resources for each.
Open the roadmapApply
Read the full posting on Hudson River Trading’s site before applying: requirements, location and pay can change after we check them.
View the posting on Hudson River Trading’s site ↗
SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with Hudson River Trading and doesn’t handle applications.