Machine Learning Engineer - Quantization

MBN Solutions
Glasgow
1 year ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer - London

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Senior ML Engineer – Startup LLM - £140k +equity – Remote (UK/EU)

Are you an expert in NLP/LLM?

Are you passionate about building groundbreaking technology and shaping the future of AI?

Do you dream of starting a company from the ground up? If so, we have the perfect opportunity for you.

The challenge

The rise of AI has skyrocketed demand for computational power. However, current solutions are expensive and dominated by large corporations. We're tackling this challenge by building a decentralized marketplace for compute power specifically designed for large language models (LLMs) with billions of parameters.

We’re an AI startup, leading the development of technology to drive the efficiency and performance of AI models. We’re looking for an AI/ML Engineer to be part of the founding team, joining our CEO, CTO, COO and Principal Engineer.

About you

You’ll draw on your experience as a NLP/AI Engineer to drive the design of the platform and tailor it to be most efficient for LLMs.

You’ll have experience building large models, fine-tuning and training them along with a strong theoretical understanding of how they optimise them you’ll be familiar with things like swarm parallelism and understand how to reduce active parameters. You’ll be able to minimise the impact of network bandwidth, define memory footprint and estimate how cache will increase to be able to design the right optimisations.

What we are looking for is someone with:

A solid background in Foundational NLP At least 3 years’ experience of hands on developing, training and deploying NLP models Experience with High Performance Computing (NVIDIA CUDA) Experience fine tuning some of the more recent LLMs (OpenAI, Anthropic, Claude, LLaMA etc)

It would be great if you also had an academic background in AI Research and experience with LLMOps

You’ll be a founding member of the team that have secured $2.5 million dollars pre-seed, and will receive equity in the business. You’ll be working remotely, with others in the team being spread across, UK, US (East Coast), Dubai and Europe.

Please note: you must be eligible to work in the UK or EU to be considered for this position.

Interested?

If you think you fit the bill, get in touch by clicking the ‘apply now’ button or get in touch with me by the following:

Email me at Call me on

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.

Jobs

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Education Jobs

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Jobs

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.