Machine Learning Engineer

Better Placed Ltd - A Sunday Times Top 10 Employer in 2023!
Birmingham
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Head of Data Science

Data Engineer - (Python, SQL, Machine Learning) - Robotics

Data Engineer - (Python, SQL, Machine Learning) - Robotics

Data Engineer - DV Cleared

Data Engineer - DV Cleared

Machine Learning Engineer

Remote (UK only)

£90,000 - £110,000 +ISO options from day 1


**ideally you'll possess a degree in Computer Science / Mathematics (or similar) from a top university and worked for an AI native or AI focussed business.


Better Placed Tech has partnered with a Microsoft backed AI business that has exited stealth mode and is building next-gen LLMs. They were founded in Silicon valley and with another funding round in 2025 they are now looking to grow out their UK based team.


The founding team is composed of industry leaders and innovators taken from some of the best-known tech businesses and educational institutions on the globe. They’re working on cutting edge technologies that are revolutionizing the AI landscape. Now is the time for an experienced ML Engineer to come on board and be a key part of the UK team.


This role is fully remote, but it would be good if you are open to travelling to Silicon Valley 1-2 times per year for collaboration.


The Job


You’ll be a talented, motivated ML Engineer with several years of experience in a native AI start up. As a key UK hire you will lead on training next gen models alongside an established US team. You’ll be the go to person in the UK team for all things ML.


Required Skills and Experience:


  • Master’s Degree in Computer Science, Machine Learning, Mathematics, or a related field, with a strong focus on NLP or ML.


  • Proficiency in MLOps best practices, including model versioning, CI/CD pipelines, containerization, and cloud deployment for large-scale models.


  • Solid programming skills in Python and familiarity with machine learning frameworks like TensorFlow, PyTorch, Hugging Face Transformers, and MLOps tools (e.g., MLflow, Kubeflow).


  • Strong analytical and problem-solving skills, with an aptitude for translating complex theoretical research into practical applications.


Day to Day


  • Conduct research and implementation on the development, training, and deployment of large language models, with a willingness to work on both pre-training and post-training (fine-tuning, alignment, optimization) processes.


  • Collaborate closely with US researchers teams to build, optimize, and maintain data sets and scalable training and data pipelines for LLMs.


  • Build and maintain documentation for infrastructure components and systems


  • Design and implement systems for reproducibility and traceability in data preparation


  • Develop and maintain documentation and codebases.


  • Stay current with advancements in machine learning, NLP, and AI, and bring them to future projects


This is a truly unique opportunity to work with some of the brightest minds in the industry on a ground-breaking project, for a confidential discussion please apply with an up to date CV.

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.

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 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.