At a Glance
- Tasks: Develop cutting-edge ML models for telecoms and tackle complex challenges in wireless technology.
- Company: Innovative international tech firm shaping the future of connectivity.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Why this job: Join a pioneering team and make a real impact in AI/ML innovation.
- Qualifications: Deep understanding of machine learning fundamentals and experience in research or engineering.
- Other info: Be part of a foundational team with excellent career advancement potential.
The predicted salary is between 36000 - 60000 £ per year.
Location: Must be based London, UK
Employment Type: Permanent
Work Model: Hybrid (London)
Introduction:
Our client (international) is a collection of engineers and designers who want the world to connect beautifully. They have a deep understanding of mobile networks and the challenges customers face every day and they're putting this knowledge to work, engineering and designing better ways to connect. They are establishing a new AI/ML engineering team in London to extend these capabilities. This is a foundational role, you will be among the first hires, helping to shape the technical direction, culture, and practices of a function at the forefront of wireless technology innovation.
We are seeking individuals with a deep, first-principles understanding of machine learning, not simply experience integrating APIs or applying pre-built models, but genuine expertise in how and why these systems work. The ideal candidate will bring experience from research or academic environments and can apply that rigour to delivering production-ready solutions.
Role Overview:
This is an opportunity to help build something from the ground up. As part of a newly formed AI/ML team, you will play a central role in establishing how our client approaches machine learning, from research methodology to engineering practices to team culture. This is not a role where you will inherit existing systems; you will be creating them. The role requires expertise that spans both research and engineering. The ideal candidate will have invested significant time developing a thorough understanding of machine learning fundamentals, whether through academic study, industry research, or rigorous self-directed learning, and will have demonstrated experience applying that knowledge to build production systems.
We are looking for practitioners who understand the mechanics beneath the abstractions, not those whose experience is limited to high-level tooling and prepackaged solutions. The role encompasses the full spectrum of ML development: researching and prototyping novel approaches where existing methods are insufficient, and engineering robust solutions that operate reliably at scale. You will work with telecommunications data including time series, network telemetry, and sensor data to address complex operational challenges in wireless technology.
What You'll Do:
- Develop ML models for telecoms and hardware applications: anomaly detection, predictive maintenance, demand forecasting, network optimisation, signal processing
- Research novel approaches when existing methods fall short, read papers, run experiments, iterate
- Implement algorithms from scratch when needed; understand what's happening under the hood
- Take models from research prototype through to production deployment
- Work with large-scale time series, sensor data, and network telemetry
- Collaborate with hardware and network engineers to understand problems deeply
- Design rigorous experiments and evaluation frameworks
- Contribute to technical direction and help shape how we build ML here
What We're Looking For:
First-Principles Understanding
Candidates must demonstrate substantive depth in ML fundamentals, including the ability to:
- Explain the mechanics and rationale behind core algorithms, gradient descent, backpropagation, attention mechanisms, regularisation techniques
- Understand the mathematical foundations underpinning these concepts, including linear algebra, calculus, and probability theory
- Reason about model behaviour from first principles during analysis and debugging
- Read research papers and implement key concepts independently
- Evaluate when different approaches are appropriate and articulate associated tradeoffs
The path to this understanding is less important than the understanding itself. Formal academic training, industry research experience, and rigorous self-directed study are all valid routes.
Machine Learning Researcher in London employer: Bullock Tech Talent Partners
Contact Detail:
Bullock Tech Talent Partners Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Researcher in London
✨Tip Number 1
Network like a pro! Attend meetups, conferences, or workshops related to AI/ML. It's a great way to meet industry folks and get your name out there. Plus, you never know who might be looking for someone just like you!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those that demonstrate your understanding of machine learning fundamentals. This will give potential employers a taste of what you can do and how you think.
✨Tip Number 3
Don’t shy away from reaching out directly! If you see a company you love, drop them a message on LinkedIn or their website. Express your interest in their work and how you could contribute to their AI/ML team.
✨Tip Number 4
Keep learning and stay updated! The AI/ML field is always evolving, so make sure you're keeping up with the latest research and trends. Join online courses or webinars to sharpen your skills and show that you're committed to growth.
We think you need these skills to ace Machine Learning Researcher in London
Some tips for your application 🫡
Show Your Passion for ML: When writing your application, let your enthusiasm for machine learning shine through! Share specific projects or research that sparked your interest and how they relate to the role. We love seeing candidates who are genuinely excited about the field.
Be Clear and Concise: Keep your application straightforward and to the point. Use clear language to explain your experience and skills, especially those related to ML fundamentals. We appreciate a well-structured application that makes it easy for us to see your qualifications.
Highlight Relevant Experience: Make sure to emphasise any hands-on experience you have with developing ML models or working with telecom data. Whether it's from academic projects or industry work, we want to know how you've applied your knowledge in real-world scenarios.
Apply Through Our Website: We encourage you to submit your application through our website. It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at Bullock Tech Talent Partners
✨Know Your ML Fundamentals
Make sure you can explain the core algorithms and their mechanics. Brush up on concepts like gradient descent, backpropagation, and regularisation techniques. Being able to discuss these topics confidently will show that you have a solid understanding of machine learning fundamentals.
✨Showcase Your Research Skills
Prepare to discuss any research projects you've worked on, especially those that required you to read papers and implement key concepts. Be ready to explain your thought process and how you approached problem-solving in those scenarios. This will demonstrate your ability to innovate and think critically.
✨Demonstrate Practical Application
Be prepared to talk about how you've taken models from research prototypes to production deployment. Share specific examples where you’ve implemented algorithms from scratch and the challenges you faced. This will highlight your hands-on experience and technical prowess.
✨Understand the Bigger Picture
Familiarise yourself with the telecommunications data you'll be working with, such as time series and network telemetry. Show that you understand the operational challenges in wireless technology and how your skills can help address them. This will illustrate your readiness to contribute to the team’s goals.