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- Tasks: Join our ML team to drive innovation in machine learning and enhance research workflows.
- Company: Dynamic finance firm with a passion for technology and collaboration.
- Benefits: Competitive salary, flexible work environment, and opportunities for professional growth.
- Why this job: Make an impact in finance using cutting-edge ML techniques and tools.
- Qualifications: Strong mathematical background and experience with ML frameworks like PyTorch.
- Other info: Curious minds welcome; no fixed qualifications required!
The predicted salary is between 36000 - 60000 £ per year.
We are looking for an engineer with robust experience in machine learning and strong mathematical foundations to join our growing ML team and to help drive the direction of our ML platform. Our ever-evolving trading environment serves as a unique, rapid-feedback platform for ML experimentation, allowing us to incorporate new ideas with relatively little friction. Our 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.
We’ll rely 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 we apply the right tool for the problem at hand. Your work will also focus on enhancing research workflows to tighten our feedback cycles. Successful ML engineers will be able to understand the mechanics behind various modelling techniques, while also being able to break down the mathematics behind them.
If you have a curious mind and a passion for solving interesting problems, we have a feeling you’ll fit right in. We’re looking for someone with:
- 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 optimisation theory, regularisation 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 organised research codebase that produces robust, reproducible results while maintaining ease of use
- Expertise wrangling an ML framework – we’re fans of PyTorch, but we’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 we’re taking the right approaches and using the right tools
- Fluency in English
Machine Learning Engineer in London employer: Jane Street
Contact Detail:
Jane Street Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the ML community, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, whether it's a GitHub repo or a personal website. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical knowledge and be ready to discuss your approach to solving problems. Practise explaining complex concepts in simple terms – it shows you really understand your stuff.
✨Tip Number 4
Don’t forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Plus, it’s a great way to ensure your application gets into the right hands.
We think you need these skills to ace Machine Learning Engineer in London
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with machine learning and any relevant projects you've worked on. We want to see how you’ve applied your knowledge in real-world scenarios, so don’t hold back!
Math Matters: Since a strong mathematical foundation is key for this role, be sure to mention any specific areas of expertise like optimisation theory or linear algebra. We love seeing candidates who are passionate about the maths behind ML!
Keep It Organised: Talk about your experience with maintaining a clean and organised research codebase. We appreciate candidates who can produce robust results while keeping things user-friendly, so share your best practices!
Stay Curious: Let us know how you keep up with the latest in the ML world. Whether it’s reading academic papers or experimenting with new tools, we’re looking for that spark of curiosity that drives innovation. Apply through our website and show us what you’ve got!
How to prepare for a job interview at Jane Street
✨Know Your ML Stuff
Make sure you brush up on your machine learning concepts, especially the ones mentioned in the job description like neural networks and gradient-boosted trees. Be ready to discuss how you've applied these techniques in real-world scenarios, as this will show your depth of knowledge and practical experience.
✨Show Off Your Math Skills
Since a strong mathematical foundation is key for this role, be prepared to talk about optimisation theory, linear algebra, and regularisation techniques. You might even want to bring examples of how you've used these concepts in your previous projects to demonstrate your understanding.
✨Demonstrate Your Passion
Let your enthusiasm for machine learning shine through! Talk about the latest research papers you've read or any new ML tools you've experimented with. This shows that you're not just qualified but also genuinely interested in the field and eager to keep learning.
✨Organise Your Codebase
Be ready to discuss how you maintain an organised research codebase. Share specific examples of how you've ensured reproducibility and ease of use in your projects. This will highlight your attention to detail and your ability to create robust solutions.
