Machine Learning Researcher
Salary not listed • Hong Kong, Hong Kong • Full-time
Posted 2y ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
Job Location
Hong Kong, Hong Kong
Also open in London, England, United Kingdom, New York, New York, United States
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
We are seeking intelligent and curious individuals to join our Machine Learning team and advance our ML initiatives. You will develop deep learning models that drive our trading strategies, leveraging a substantial computing cluster with numerous high-end GPUs. The unique challenges of trading, including large models, non-stationary datasets, and competitive multi-agent environments, necessitate the exploration of novel techniques. Collaboration is key, with researchers, engineers, and traders working closely to train models, design systems, and execute trading strategies. Your role will involve analyzing market data, optimizing hyperparameters, troubleshooting distributed training, and studying model behavior in production. You will contribute to shaping the future of ML at Jane Street by training next-generation deep learning models for trading strategies and building foundational knowledge for new markets.
Responsibilities
- Build deep learning models for trading strategies.
- Train models for next-generation trading strategies.
- Architect systems and run trading strategies.
- Debug distributed training performance.
- Study model behavior in production.
- Shape the future of ML at the company.
- Build fundamental understanding to tackle new markets and situations.
- Hire new colleagues.
- Attend conferences.
- Teach techniques to teammates.
Requirements
- Practical experience working on empirical ML problems.
- Ability to apply logical and mathematical thinking to problems.
- Intellectual curiosity and excitement about state-of-the-art research across ML problem domains.
- Fluency with a versatile set of models and tricks.
- Hands-on coding skills for rapid implementation and iteration in Python and an ML framework.
- Eagerness to ask questions, admit mistakes, and learn new things.
- Fluency in English.