Description Digital Catapult is looking for a Senior Technology Specialist in AI to join our team of experts in applying cutting edge AI to real world challenges. In this pivotal role, you’ll be the technical bridge between complex requirements and high-impact designs, turning visionary concepts into robust digital realities for a diverse range of stakeholders. This initial focus of the...
Digital Catapult
Belfast
Machine Learning Engineer (Forward Deployed)
We’re looking for a Machine Learning Engineer (Forward Deployed) to join a supportive, multidisciplinary team delivering real-world AI/ML systems into operational environments. In this role, you’ll lead software deployments, working closely with users and stakeholders to translate their problems into robust, production-ready machine learning solutions. You’ll rapidly explore, prototype, and deploy ML approaches both within and beyond our core product...
Mind Foundry
Oxford/ Hybrid
Associate Director, AI & Advanced Analytics
CSL's R&D organization is accelerating innovation to deliver greater impact for patients. With a project-led structure and a focus on collaboration, we’re building a future-ready team that thrives in dynamic biotech ecosystems. Joining CSL now means being part of an agile team committed to developing therapies that make a meaningful difference worldwide. Could you be our next Associate Director, AI...
Machine Learning Engineer - £110k – £130k – Geospatial Tech 4 GoodMachine Learning | Deep Learning | Time Series | Climate | Remote Sensing | PyTorch | scikit‑learn | Geospatial | AWS | MLOps | Python | Risk Modelling | FinTech |Do you want to work with a business building AI‑native data system that bring clarity and credibility to nature‑based...
Opus Recruitment Solutions
London
Machine Learning Engineer / MLOps Engineer
Machine Learning Engineer (MLOps)Location: Oxfordshire, UKPermanentHYBRID 3 days per week onsiteARCA Resourcing is partnering with an innovative, established but scaling technology company in Oxfordshire to recruit a Machine Learning Engineer (MLOps). This role offers the opportunity to work at the forefront of advanced computing and emerging technologies, applying modern machine learning techniques to complex scientific and engineering challenges.As a Machine...
ARCA Resourcing Ltd
Oxford
Machine Learning Engineer
Your Responsibilities: Design and deliver scalable AI and machine learning solutions across underwriting, risk, and operationsOwn the end-to-end ML lifecycle, from feature engineering to deployment and monitoringBuild and maintain data pipelines and production workflows using Python, TensorFlow, PyTorch, scikit-learn, AWS (S3, Lambda, SageMaker, Step Functions, Bedrock), Snowflake, and DataikuApply MLOps best practices, including CI/CD, automated testing, model versioning, and observabilityDefine...
Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services.
For professionals exploring opportunities on www.MachineLearningJobs.co.uk
, understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.
Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more.
With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive.
Here’s the honest truth most machine learning hiring managers won’t say out loud:
👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know.
Tools are important — no doubt — but context, judgement and outcomes matter far more.
So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped.
This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.
Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio.
This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.
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