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- Tasks: Develop advanced ML systems to solve complex challenges in finance.
- Company: Leading financial technology firm in Greater London.
- Benefits: Dynamic environment with opportunities for significant impact on large-scale projects.
- Why this job: Work with cutting-edge tech and collaborate with top teams in finance.
- Qualifications: Proficiency in Python/C++ and experience with GPU programming.
- Other info: Fast-paced role perfect for those driven by technical challenges.
The predicted salary is between 48000 - 72000 £ per year.
A leading financial technology firm in Greater London is seeking world-class engineers to develop advanced ML systems that tackle complex challenges in quantitative finance. You will work with cutting-edge technologies and collaborate with trading, research, and engineering teams.
Ideal candidates should have proficiency in Python and/or C++, and experience with GPU programming. This role offers a dynamic and fast-paced environment where you can make significant impacts on large-scale ML projects, so if you are driven by technical challenges, we want to hear from you.
Campus ML Research Engineer: Build Large-Scale AI Systems in London employer: Jump Trading
Contact Detail:
Jump Trading Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Campus ML Research Engineer: Build Large-Scale AI Systems in London
✨Tip Number 1
Network like a pro! Reach out to current employees at the firm on LinkedIn or attend industry meetups. A friendly chat can give us insights into the company culture and maybe even a referral!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects in Python or C++. Share your GitHub link during interviews to demonstrate your hands-on experience with ML systems.
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills. Practice common algorithms and data structures, and don’t forget to review GPU programming concepts that are relevant to the role.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who take the initiative to connect directly with us.
We think you need these skills to ace Campus ML Research Engineer: Build Large-Scale AI Systems in London
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your proficiency in Python and/or C++ right from the start. We want to see how your technical skills can tackle those complex challenges in quantitative finance!
Tailor Your Application: Don’t just send a generic application! Take the time to tailor your CV and cover letter to reflect the specific requirements of the Campus ML Research Engineer role. We love seeing candidates who understand what we’re looking for.
Be Clear and Concise: When writing your application, keep it clear and to the point. We appreciate well-structured applications that get straight to the heart of your experience and how it relates to the role.
Apply Through Our Website: We encourage you to apply through our website for the best chance of getting noticed. It’s the easiest way for us to keep track of your application and ensure it reaches the right team!
How to prepare for a job interview at Jump Trading
✨Know Your Tech Inside Out
Make sure you brush up on your Python and C++ skills before the interview. Be ready to discuss specific projects where you've used these languages, especially in relation to GPU programming. This will show that you’re not just familiar with the tech, but that you can apply it effectively in real-world scenarios.
✨Understand the Financial Context
Since this role is in a financial technology firm, it’s crucial to have a grasp of quantitative finance concepts. Do some research on how ML is applied in finance, and be prepared to discuss how your skills can help tackle complex challenges in this field. This will demonstrate your genuine interest in the industry.
✨Prepare for Technical Challenges
Expect to face technical questions or coding challenges during the interview. Practice solving problems related to large-scale ML systems and be ready to explain your thought process. This will help you showcase your problem-solving skills and your ability to work under pressure.
✨Show Your Collaborative Spirit
This role involves working closely with trading, research, and engineering teams. Be prepared to share examples of how you’ve successfully collaborated in the past. Highlight your communication skills and your ability to work in a fast-paced environment, as this will be key to making a significant impact on projects.
