Senior Machine Learning Engineer – Computer Vision

WatersEdge Solutions
London
3 months ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer, Search & Recommendations

Senior Machine Learning Scientist

Senior Machine Learning Scientist

Senior Forward-Deployed Data Scientist

Location: Remote
Employment Type: Full-Time
Industry: AI & Machine Learning | Identity Verification | Computer Vision


WatersEdge Solutions is partnering with an AI-driven identity verification platform to recruit a Senior Machine Learning Engineer with deep expertise in computer vision and large language models. This is a high-impact role at the core of an innovation-focused team transforming how digital identity is verified across global emerging markets.


About the Role


As a Senior Machine Learning Engineer, you’ll design and optimise machine learning models for identity documents, biometric data, and natural language processing. You’ll work across the full development lifecycle—from research to deployment—while mentoring other engineers and helping to define the technical roadmap. This role is perfect for an experienced ML specialist with a passion for real-world AI solutions and scalable impact.


Key Responsibilities




  • Design and develop machine learning algorithms for computer vision and LLM-based document analysis




  • Build and maintain scalable ML pipelines for data preparation, model training, and deployment




  • Collaborate with software engineers to integrate ML solutions into production systems




  • Lead research into new AI methodologies and technologies, keeping pace with trends




  • Analyse large-scale data to improve model performance and product outcomes




  • Mentor junior engineers and lead ML-focused initiatives from concept to deployment




What You’ll Bring




  • Master’s degree or higher in Computer Science, Machine Learning, or related field




  • 6+ years’ experience in machine learning with strong emphasis on computer vision




  • Proficiency in Python and ML libraries (TensorFlow, PyTorch)




  • Deep understanding of image processing, linear algebra, and probabilistic modelling




  • Proven experience in deep learning (CNNs, Transformers, DNNs)




  • Hands-on experience with LLMs (e.g., GPT, BERT) for NLP or document processing




  • Familiarity with cloud tools such as AWS SageMaker and Lambda




  • Experience with Docker, Git, and Jira




  • Strong documentation and unit testing practices




Nice to Have




  • Background in cybersecurity or biometrics




  • Proficiency in C++ for performance-intensive applications




What’s On Offer




  • Competitive salary with equity and bonus opportunities




  • High-impact work shaping the future of trust and identity in digital ecosystems




  • Collaborative, fast-paced team culture with deep tech roots




  • Full-remote flexibility and support for continuous learning




Company Culture


At WatersEdge Solutions, we align visionary engineers with companies building the next frontier of AI and digital security. Join a team that thrives on innovation, collaboration, and technical excellence—where your work directly impacts millions globally.


 


If you have not been contacted within 10 working days, please consider your application unsuccessful. 

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

Jobs

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

Education

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.