1000+ Best de part time Jobs in Cambridge (enero 2026) - page 3

Machine Learning Researcher

hace 21 horas

Cambridge

Job Title: AI/ML Research Engineer (London, UK) 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. Their team first came together to build one of the world's first Massive MIMO 5G networks from the first base station to the first 5G customer. Today, 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 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.

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