At a Glance
- Tasks: Design and deploy advanced machine learning models for quantitative trading.
- Company: Join a high-performing quantitative trading firm with a remote-first culture.
- Benefits: Competitive salary, flexible remote work, and opportunities for professional growth.
- Other info: Collaborate with top engineers and traders in a dynamic, innovative environment.
- Why this job: Make a real impact on trading performance using cutting-edge machine learning technology.
- Qualifications: Degree in a quantitative field and strong experience in Python ML frameworks.
The predicted salary is between 60000 - 80000 £ per year.
This is a unique opportunity for a Senior Machine Learning Engineer to join a high-performing quantitative trading firm building next-generation machine learning systems for financial markets. Working alongside a team of exceptional engineers, researchers, and traders, you will play a key role in developing models and infrastructure that directly influence trading performance and investment decisions.
While the role is fully remote, candidates must be based in the UK and willing to attend occasional team gatherings and meetings.
You will design, develop, and deploy advanced machine learning models across a range of quantitative trading and research initiatives. Working closely with trading, data, and engineering teams, you will help build scalable ML systems capable of extracting signals from large and complex datasets while contributing to the long-term technical direction of the platform.
- A degree (preferably MSc or PhD) in Mathematics, Statistics, Computer Science, Physics, Engineering, or a related quantitative discipline
- Strong commercial experience developing machine learning models in Python using frameworks such as PyTorch, JAX, or TensorFlow
- Experience building predictive models for time-series, forecasting, signal generation, or large-scale data analysis
- Proven ability to take models from research through to live deployment
- Experience within quantitative trading, hedge funds, systematic investing, market making, or financial technology
- Experience with reinforcement learning, probabilistic modelling, Bayesian methods, or deep learning for financial applications
- Familiarity with market data, alternative datasets, and low-latency or high-performance systems
Machine Learning Performance Engineer employer: Platform Recruitment
Join a leading quantitative trading firm that champions innovation and excellence in the financial markets. As a Senior Machine Learning Engineer, you will thrive in a collaborative remote environment, where your contributions directly impact trading performance and investment strategies. With a strong focus on employee growth, cutting-edge technology, and a culture of teamwork, this role offers a unique opportunity to advance your career while working alongside some of the brightest minds in the industry.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Performance Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can get your foot in the door.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. We recommend including any models you've built or deployed, especially those related to financial markets. This will give potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills and understanding of quantitative trading. We suggest practicing common ML interview questions and being ready to discuss your past projects in detail.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, it shows you’re genuinely interested in joining our team.
We think you need these skills to ace Machine Learning Performance Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the role of a Machine Learning Performance Engineer. Highlight your experience with Python and any relevant frameworks like PyTorch or TensorFlow, as well as your background in quantitative trading or financial technology.
Showcase Your Projects:Include specific projects where you've developed machine learning models, especially those that relate to time-series analysis or signal generation. We want to see how you've taken models from research to live deployment!
Craft a Compelling Cover Letter:Your cover letter should reflect your passion for machine learning and finance. Share why you're excited about this opportunity at StudySmarter and how your skills align with our mission to build next-generation systems.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting role. Don’t miss out on the chance to join our team!
How to prepare for a job interview at Platform Recruitment
✨Know Your Models Inside Out
Make sure you can discuss your machine learning models in detail. Be prepared to explain the algorithms you've used, why you chose them, and how they performed in real-world scenarios. This shows not only your technical expertise but also your ability to apply theory to practice.
✨Brush Up on Financial Knowledge
Since this role is within a quantitative trading firm, having a solid understanding of financial markets and trading strategies will set you apart. Familiarise yourself with concepts like time-series analysis and signal generation, as well as any relevant market data you might encounter.
✨Showcase Your Teamwork Skills
Collaboration is key in this role, so be ready to share examples of how you've worked effectively with engineers, researchers, or traders in the past. Highlight any projects where you contributed to a team effort, especially those that led to successful model deployment.
✨Prepare for Technical Questions
Expect to face technical questions related to Python, machine learning frameworks, and possibly even coding challenges. Brush up on your coding skills and be ready to demonstrate your problem-solving abilities on the spot. Practising common interview questions can help you feel more confident.
