AI Research Engineer, Pre-Training
An engineering role on HRT's AI Labs (HAIL) team, focused on large-scale model training on the firm's GPU clusters. The aim is to get the most out of the hardware for models used in trading.
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.
- Develop and optimise training kernels
- Improve data loading and parallelism for big training jobs
- Work on networking and fault tolerance in the cluster
- Co-design model changes with researchers
What they ask for
Required
- GPU kernel skills (CUDA, Triton, Pallas or CuTe)
- Low-level PyTorch, JAX or XLA work
- Two or more years building deep learning systems
- Ability to move methods between domains
Preferred
- LLM experience
- FPGA or ASIC experience
- Education
- No degree level stated.
- Experience
- 2+ years building deep learning systems; finance experience not required
- Languages and tools
How to prepare
- Train a transformer across several GPUs using data and tensor parallelism
- Write and profile a custom GPU kernel
- Study fault-tolerant checkpointing for long runs
- Build a fast streaming data loader and measure GPU utilisation
What to study
- Python and Data Primer
- Financial Machine Learning
- Quantitative Data Engineering
- Linear Algebra, Optimisation and Numerical Methods
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.