Machine Learning Engineer

Weare5vtech
Bristol
9 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer - Satellite

Machine Learning Engineer

  • Paris, France
  • CDI, Permanent position
  • Hybrid working

5V Tech is working with an innovative and rapidly expanding scale-up operating at the intersection of healthcare and AI. They’re now looking for aMachine Learning Engineerto join their team in Paris.

This is an exciting opportunity to apply your technical expertise in a fast-paced, regulated environment, building impactful solutions that support real-world clinical needs. You’ll be joining a collaborative, cross-functional team with a shared mission to push the boundaries of digital healthcare.

Your main responsibilities will include:

  • Designing and validating robust ML models that align with medical regulations and clinical requirements.
  • Collaborating with software engineers and product teams to integrate models into scalable production systems.
  • Building internal tools and frameworks to support model testing, monitoring, and performance tracking.
  • Producing clear and structured technical documentation for both internal and external use.

Key skills required:

  • Solid background in Python, Linux, and Git.
  • Experience in deep learning (TensorFlow, Keras, PyTorch) and statistical validation of medical devices.
  • Fluent in English and French.

If this sounds like the kind of opportunity you’ve been looking for, apply now or reach out directly for a confidential conversation.

#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.