Machine Learning Engineer

Skillsearch
London
10 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer / MLOps Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Job Description

Company Overview:

We are partnering with an innovative and forward-thinking game development studio, known for creating captivating and technically advanced games that engage a global audience. The studio is driven by a passion for crafting meaningful gaming experiences and pushing the boundaries of whats possible. They are now looking for talented engineers to join their team and contribute to exciting, ground breaking projects. If you’re ready to make an impact in the gaming world, this is an incredible opportunity.

Role Overview:

As a Software Engineer with this studio, you will play a key role in the development of cutting-edge features that utilize Machine Learning to create dynamic, procedurally generated game worlds. You will collaborate with a dedicated and forward-looking team to design, implement, and iterate on new features, helping to shape the future of their games.

Key Responsibilities:

  • Lead the development of new and existing features utilizing Machine Learning to generate immersive, procedurally generated worlds within the games.
  • Collaborate with a humble, empathetic, and ambitious development team to push the boundaries of innovation in the gaming space.
  • Drive the design and iteration of features, incorporating feedback from peers and stakeholders to refine and improve your work.
  • Manage your workload autonomously, prioritizing tasks and ensuring timely delivery of high-quality results.
  • Communicate effectively within the team, sharing progress, challenges, and insights while being receptive to feedback.

Qualifications:

  • Strong software engineering skills, with a solid understanding of algorithms, data structures, and software architecture.
  • Experience with Machine Learning engineering, particularly with modern libraries such as PyTorch.
  • Deep knowledge of generative models, including expertise in diffusion models and Variational Autoencoders (VAEs).
  • Proven ability to work autonomously, take ownership of tasks, and drive them to completion.
  • Strong communication skills, with the ability to collaborate effectively within a team-oriented environment.
  • Able to prioritize tasks and adapt to changing circumstances in a fast-paced, evolving environment.

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.