Quant · Quantitative trading firm
Jane Street has 31 active AI-related job listings. The majority of these roles are focused on data (29%) and pre-training (26%), with a significant number also in serving infrastructure (23%). Research roles represent the largest functional area, with hiring concentrated in Hong Kong and the United States. Frequent tech tags include fine-tuning, model serving, and pretraining.
Currently tracking 29 active AI roles, with 38 new openings in the last 4 weeks. Primary focus: Pretrain · Research.
Jane Street currently has 18 active AI-related roles in our index. The most common open titles are: Machine Learning Researcher (5), Quantitative Researcher (4), Machine Learning Engineer (3), Machine Learning Performance Engineer (3), Machine Learning Educator. Most positions are in Research and Engineering.
Jane Street's active AI hiring is concentrated in: serving infrastructure (39%), pre-training (28%), data (28%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Jane Street is hiring AI talent in: Hong Kong (8 roles), United States (5 roles), United Kingdom (4 roles), Singapore (1 role).
Job postings at Jane Street most frequently mention: Machine Learning, Statistics, GPU Computing, Real-Time Systems, Distributed Training.
In the past 30 days, Jane Street has posted 0 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Quantitative Researcher Jane Street is seeking Quantitative Researchers to build models, strategies, and systems for pricing and trading financial instruments. The role involves applying expertise in experiment design, dataset generation, time series analysis, feature engineering, and model building to financial datasets. Researchers will collaborate with engineers and traders, working with large datasets and a significant GPU cluster. The position is open to various statistical and machine learning techniques, emphasizing curiosity and the ability to integrate findings into actionable trading strategies. | Serve | 7 |