Computer Vision and Machine Learning Researcher - / C++ / Python / Tensorflow / PyTorch / Publications

European Tech Recruit
Woking
10 months ago
Applications closed

Related Jobs

View all jobs

Principal Data Scientist & Machine Learning Researcher

Machine Learning Engineer/Researcher - 2026 Graduate Programme

Junior Machine Learning Engineer

Senior Data Scientist - NLP AI Research

Senior Data Scientist - NLP AI Research

Machine Learning Research Engineer - NLP / LLM

Computer Vision and Machine Learning Researcher - / C++ / Python / Tensorflow / PyTorch / Publications


  • Do you have a solid experience in Machine Learning and Computer Vision with programming experience in C++ and Python?
  • Solid Research background with publications in ICML, NeurIPS, ICLR, CVPR, ECCV, IEEE TPAMI, AAAI or similar?
  • Experience developing with machine learning frameworks such as Tensorflow and/or Pytorch
  • Do you want to join a globally recognised mobile/tech development company?


We are seeking aComputer Vision and Machine Learning Researcherwith experience in the fundamentals in machine learning, NLP and Computer Vision and a solid track record of publications in top ML/AI conferences/journals such as ICML, NeurIPS, ICLR, CVPR, ECCV, IEEE TPAMI, AAAI or similar and experience in at least one of the following topics: Generative AI, or 3D vision to join our client in the northwest Surrey/West London (1 hour from King's Cross) on a initial 6 month contract (PAYE) basis.


Please note- as this is a contract position, we can only consider applicants with full Right to Work in the UK and with a maximum of a 1 month notice period.


Required skills:

  • Masters or higher degree in ML/AI, Computer Science/Engineering, or related disciplines
  • Strong fundamentals in machine learning, NLP and Computer Vision
  • First author publications in top ML/AI conferences/journals (e.g., ICML, NeurIPS, ICLR, CVPR, ECCV, IEEE TPAMI, AAAI or similar)
  • Strong development skills with Python and/or C/C++
  • Demonstrated experience in: Generative AI, including hands-on implementation of state-of-the-art models or 3-D vision
  • Developing with machine learning frameworks – Tensorflow/Pytorch


Any of the following would be considered a plus:

  • Expertise in image-based 3D reconstruction: Photogrammetry, Neural Radiance Fields (NERF) or Gaussian Splatting techniques.
  • Model optimization and knowledge distillation.
  • Experience in computer graphics and rendering: design and development of software such as OpenGL, OpenGL ES, Vulkan or DirectX
  • Experience in Android application development


If this sounds interesting and you'd like to learn more, click the link below to apply or email me with a copy of your resume on


By applying to this role you understand that we may collect your personal data and store and process it on our systems. For more information please see our Privacy Notice (https://eu-recruit.com/about-us/privacy-notice/)

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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.