Artificial Intelligence Engineer

Nexus Additive
London
9 months ago
Applications closed

Related Jobs

View all jobs

Computer Vision and Artificial Intelligence Engineer

Research Software Engineer: Geospatial Artificial Intelligence (Geo-AI)

Software Engineer, Applied Artificial Intelligence (AI)

Software Engineer, Applied Artificial Intelligence (AI)

Geospatial Artificial Intelligence Research Scientist

Artificial Intelligence Manager (18-month FTC)

About Nexus

Nexus is a cutting-edge deep tech startup emerging from Imperial College London, positioned at the intersection of advanced manufacturing and artificial intelligence. We're solving the billion-pound challenge of metal 3D printing qualification and certification that has limited the industry's full potential. Our proprietary technology transforms complex sensor data into real-time actionable insights, enabling manufacturers to dynamically adapt and verify their processes with unprecedented precision.

Founded by a team of experts in AI, materials science, and additive manufacturing, we've already secured partnerships with industry leaders and are backed by investors who believe in our vision to fundamentally transform how critical components are manufactured.


The Opportunity

This is not just another AI role. This is your chance to redefine manufacturing.

As our AI engineer, you'll be at the forefront of developing novel computational approaches that are already changing how aerospace, medical, and automotive manufacturers produce their most critical components. You'll join a founding-stage technical team where your contributions will have immediate, visible impact across our product and technology roadmap.


Core Responsibilities

  • Architect and train sophisticated models using our core technology stack to extract meaningful patterns from multi-modal sensor data
  • Pioneer novel pre-training and data-augmentation strategies to achieve exceptional generalisation with limited training data
  • Develop innovative evaluation frameworks that quantify model uncertainty and reliability for safety-critical applications
  • Partner with our software team to solve complex deployment and inference challenges in resource-constrained environments

 

Required Expertise

  • PhD in computer science, physics, engineering, or related field with significant machine learning focus
  • 2+ years of hands-on experience building and deploying real-world AI systems that solved genuine business problems
  • Mastery of machine learning fundamentals with deep understanding of model architecture design
  • Proven experience with advanced deep learning concepts such as CNNs, time-series models, and transfer learning
  • Exceptional problem-solving abilities with a bias toward practical solutions
  • Eligible to work in the UK


Preferred Qualifications

  • Experience deploying AI in regulated or safety-critical environments (aerospace, medical devices, automotive, etc.)
  • Published research at top-tier AI conferences (NeurIPS, ICML, ICLR, CVPR, ECVA, etc.)
  • Experience optimizing models for deployment in C++ or other performance-critical environments
  • Background in manufacturing processes, materials science, or signal processing
  • Startup experience or demonstrated ability to thrive in ambiguous, fast-paced environments


Why Join Nexus

  • Impact: Your work will directly enable more sustainable, efficient manufacturing of critical components
  • Innovation: Work at the bleeding edge of both AI and advanced manufacturing
  • Team: Join a diverse, multi-disciplinary team of experts passionate about solving meaningful problems
  • Benefits:
  • Competitive salary
  • Comprehensive healthcare package
  • 25 days holiday plus bank holidays
  • Central London location with excellent facilities (in-person role)
  • Regular team events and learning opportunities

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.