Machine Learning Engineer (Remote)

Harrington Starr
London
4 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

A leading buy-side firm, is looking for a Machine Learning Engineer to push the boundaries of how data and models are used in systematic investing. This isnt a research support role its about building the ML infrastructure that directly drives trading strategies and PnL. Design and implement distributed training pipelines handling high-volume data and complex model architectures
Optimise and extend machine learning frameworks to improve training and inference performance
Leverage GPU programming (CUDA, cuDNN, TensorRT) to maximise efficiency
Assess and integrate emerging open-source tools to enhance ML development and deployment

5+ years experience in machine learning with a focus on training and inference systems
~ Strong programming expertise in Python and C++ or CUDA
~ Hands-on experience with GPU acceleration and distributed training (Horovod, NCCL or similar)
~ Background in real-time, low-latency ML pipelines
~ Contributions to open-source ML or distributed systems projects are advantageous


Top-of-market comp: 140,000 - 190,000 + performance bonus.
If youre a machine learning engineer who wants to operate at the sharp end of finance, building systems that actually move markets, this is the role.

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.