Machine Learning Researcher

Machine Learning Researcher

Full-Time 48000 - 72000 £ / year (est.) No home office possible
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At a Glance

  • Tasks: Develop cutting-edge ML models for telecoms and tackle complex challenges in wireless technology.
  • Company: Join an innovative international team shaping the future of 5G connectivity.
  • Benefits: Enjoy a competitive salary, hybrid work model, and opportunities for professional growth.
  • Why this job: Be a pioneer in a new AI/ML team and make a real impact on technology.
  • Qualifications: Deep understanding of machine learning fundamentals and experience in research or engineering.
  • Other info: Help shape the culture and practices of a groundbreaking function in wireless innovation.

The predicted salary is between 48000 - 72000 £ per year.

Location: Must be based in London, UK

Employment Type: Permanent

Work Model: Hybrid (London)

Introduction: Our client is a collection of engineers and designers who want the world to connect beautifully. They design and engineer a range of 5G smart routers connecting homes and businesses around the world, with their devices currently serving over a million customers globally. 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
  • 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 employer: Bullock Tech Talent Partners

As a pioneering force in the 5G technology landscape, our client offers an exceptional opportunity for Machine Learning Researchers to join a dynamic and innovative team in London. With a strong emphasis on employee growth, a collaborative work culture, and the chance to shape the future of AI/ML in telecommunications, this role provides a unique platform for meaningful contributions and professional development. The hybrid work model further enhances work-life balance, making it an attractive employer for those seeking to make a significant impact in a cutting-edge field.
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Contact Detail:

Bullock Tech Talent Partners Recruiting Team

StudySmarter Expert Advice 🤫

We think this is how you could land Machine Learning Researcher

✨Tip Number 1

Network like a pro! Attend meetups, conferences, or workshops related to AI/ML. Engaging with industry professionals can open doors and give you insights that you won't find in job descriptions.

✨Tip Number 2

Show off your skills! Create a portfolio showcasing your machine learning projects. Whether it's GitHub repos or a personal website, having tangible evidence of your expertise can make you stand out.

✨Tip Number 3

Prepare for interviews by brushing up on your fundamentals. Be ready to explain algorithms and concepts from first principles. Practising common ML interview questions can help you articulate your knowledge confidently.

✨Tip Number 4

Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Tailor your application to highlight how your skills align with the role and the company's mission.

We think you need these skills to ace Machine Learning Researcher

Machine Learning Fundamentals
Anomaly Detection
Predictive Maintenance
Demand Forecasting
Network Optimisation
Signal Processing
Research Methodology
Algorithm Implementation
Time Series Analysis
Sensor Data Analysis
Network Telemetry
Mathematical Foundations (Linear Algebra, Calculus, Probability Theory)
Experiment Design
Technical Direction

Some tips for your application 🫡

Show Your Passion for ML: When you're writing your application, let your enthusiasm for machine learning shine through! Share specific projects or research that got you excited about the field. We want to see your genuine interest and how it aligns with our mission.

Be Clear and Concise: Keep your application straightforward and to the point. Use clear language to explain your experience and skills. We appreciate a well-structured application that makes it easy for us to see how you fit into the role.

Highlight Relevant Experience: Make sure to emphasise any hands-on experience you've had with ML models, especially in telecoms or hardware applications. We’re looking for candidates who can demonstrate their ability to take concepts from research to production, so share those examples!

Apply Through Our Website: Don’t forget 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

✨Master the Fundamentals

Make sure you can explain the core algorithms and their mechanics, like gradient descent and backpropagation. Brush up on your linear algebra, calculus, and probability theory, as these are crucial for demonstrating your first-principles understanding.

✨Showcase Your Research Skills

Be prepared to discuss any research papers you've read and how you've implemented key concepts from them. This will show that you can think critically and apply theoretical knowledge to practical problems, which is essential for this role.

✨Demonstrate Problem-Solving Abilities

Think of examples where you've tackled complex challenges in machine learning or engineering. Be ready to explain your thought process and the steps you took to arrive at a solution, especially when existing methods weren't sufficient.

✨Collaborate and Communicate

Since you'll be working with hardware and network engineers, practice articulating technical concepts clearly. Show that you can collaborate effectively by discussing how you've worked in teams before and how you approach problem-solving in a group setting.

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