Senior Data Research Engineer Computer Vision

Premier IT
Oxford
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Engineer

Senior Machine Learning Engineer – Computer Vision

Lead Data Scientist

Applied AI ML - Senior Associate - Machine Learning Engineer

Lead Software Engineer - Agentic AI/Machine Learning

Head of Data Science

Computer Vision Engineer

Kidlington, Oxfordshire


Automation / Image Processing / Python


£60,000-£65,000


Company Overview

I've recently partnered with a growing Biotech business who have been going for 5+ years and looking to add a Computer Vision Engineer to their team in Kidlington! The company have developed industry leading products across the Biotech space, massively impacting healthcare, advancing medicine and developing research which is used globally by a wide range of diverse clients.


They are naturally looking to grow the team and enhance the skill level across areas like automation, computer vision, image processing. You will work with wider teams to integrate computer vision models into various applications used across the business.


The company are based in Kidlington, Oxfordshire and require office working of 4 days a week.


Technical Requirements

  • Come from a Physics or Computer Vision background.
  • Strong experience across Computer Vision and wider tech - PyTorch, Tensorflow.
  • Naturally have worked extensively with Python.
  • Confident collaborating with different teams across the business - R&D, Biology.
  • Ideally have worked with Robotic programming.
  • Ideally have worked with other programming languages such as C#/C++ - not essential.
  • Happy mentoring Junior developers where needed.
  • Confident managing and working with data to assess performance metrics.
  • Good communication skills.

Location:Kidlington, Oxfordshire, 4 days a week in the office


Benefits

Bonus, Pension, Healthcare, 25 days holiday


If this role sounds of interest, then please apply and I can give you a call.


Tim Stock
(phone number removed) | (phone number removed)
(url removed)


#J-18808-Ljbffr

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.

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.

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.

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.