Machine Learning Research Engineer
Salary not listed • London, England, United Kingdom • 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
London, England, United Kingdom
Also open in Hong Kong, Hong Kong, New York, New York, United States
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
AI Research Engineer
About the job
We are seeking a Research Engineer with a strong background in machine learning and mathematics to join our expanding ML team. You will play a key role in shaping the future of our ML platform, leveraging our dynamic trading environment for rapid experimentation and innovation. Our team values software engineering craft and the creation of user-friendly APIs and systems. Your expertise will guide our decision-making on applying the most effective ML tools, from neural networks to ensemble methods, and will focus on improving research workflows for faster feedback loops. We are looking for individuals who can deeply understand and articulate the mathematics behind various modeling techniques, acting as a crucial link between research and engineering to advance our model capabilities across the entire technology stack. This could involve scaling our technology, prototyping new research ideas with Researchers, or developing workflow APIs for diverse applications.
Responsibilities
- Drive the direction of the ML platform.
- Apply in-depth knowledge of the ML ecosystem and various approaches to aid decision-making.
- Enhance research workflows to tighten feedback cycles.
- Understand the mechanics and mathematics behind various modeling techniques.
- Act as a bridge between research and engineering to push forward model capabilities.
- Enable new scales in the technology stack.
- Rapidly prototype new research ideas with Researchers.
- Build workflow APIs that support a wide range of use cases.
- Create and maintain an organized research codebase that produces robust, reproducible results.
- Experiment with the latest hardware and read the source of new machine learning packages.
- Ask hard questions about whether the right approaches and tools are being used.
Requirements
- Experience building and maintaining training and inference infrastructure.
- Understanding of the process from concept to production.
- Strong mathematical background, including optimization theory, regularization techniques, and linear algebra.
- Passion for staying current with the state of the art in machine learning.
- Proven ability to create and maintain an organized research codebase.
- Expertise with an ML framework like PyTorch, Jax, or TensorFlow.
- Inventive approach and willingness to question existing methods.
- Fluency in English.