Machine Learning Engineer- World-Leading Prop Trading Fund

About the Position

My client is a tech-centric prop trading fund, trading a wide range of financial products across the globe. Now looking for an engineer with robust experience in machine learning and strong mathematical foundations to join the growing ML team and to help drive the direction of the ML platform.

Machine learning is a critical pillar of the fund's global business. The ever-evolving trading environment serves as a unique, rapid-feedback platform for ML experimentation, allowing new ideas to be incorporated with relatively little friction. The ML team is full of people with a shared love for the craft of software engineering, and for designing APIs and systems that are delightful to use.

You'll draw on your in-depth knowledge of the ML ecosystem and understanding of varying approaches - whether it's neural networks, random forests, gradient-boosted trees, or sophisticated ensemble methods - to aid decision-making so that the right tool is applied for the problem at hand. Your work will also focus on enhancing research workflows to tighten feedback cycles. Successful ML engineers will be able to understand the mechanics behind various modeling techniques, while also being able to break down the mathematics behind them.

If you've never thought about a career in finance, you're in good company. Many of the employees were in the same position before working at this firm. While there isn't a fixed list of qualifications they're looking for, if you have a curious mind and a passion for solving interesting problems, you'll almost certainly fit right in.

Requirements:

• Experience building and maintaining training and inference infrastructure, with an understanding of what it takes to move from concept to production
• A strong mathematical background; good candidates will be excited about things like optimization theory, regularization techniques, linear algebra, and the like
• A passion for keeping up with the state of the art, whether that means diving into academic papers, experimenting with the latest hardware, or reading the source of a new machine learning package
• A proven ability to create and maintain an organized research codebase that produces robust, reproducible results while maintaining ease of use
• Expertise wrangling an ML framework - they're fans of PyTorch, but they'd also love to learn what you know about Jax, TensorFlow, or others
• An inventive approach and the willingness to ask hard questions about whether the right approaches are being taken and the right tools being used
• Fluency in English

Whilst we carefully review all applications, to all jobs, due to the high volume of applications we receive it is not possible to respond to those who have not been successful.