Systems Engineer

Henderson Scott
Cheltenham
11 months ago
Applications closed

Related Jobs

View all jobs

Senior Computer Vision Engineer

Senior Computer Vision Engineer

Data Scientist in Power Electrical Systems

Data Scientist – Power Grids & Energy Analytics

Senior Data Science Engineer

Principal Machine Learning Engineer – Production Systems

Systems EngineerWe are currently hiring on behalf of an industry-leading engineering company who have several systems engineering roles at different levels of seniority across the business to support the development and production of their product range. In these roles, you will gain a broad engineering experience, engage with partners, co-ordinate technology specialists, and develop advanced systems engineering techniques.We are looking for candidates who have experience in some/any of the following skill areas:MATLAB/SimulinkSystems Integration and System DesignElectro-Optics/InfraredRF/Microwave systemsSimulation & ModellingAlgorithm DevelopmentRequirements engineeringConcept assessment and design trade studiesSystem architecture design and functional modellingPerformance assessment and systems behaviour analysisVerification, Validation and CertificationModel based engineering techniques e.g. MBSE or SysMLSystems Engineering tools e.g. IBM DOORS Next, RhapsodyWhat we need from you:Experience of systems engineering within a complex, high technology engineering or manufacturing environmentExperience with good systems engineering practicesGood written and verbal communication and presentation skillsGood analytical and problem-solving skillsThe ability to plan and control your workIf you would like to know more details about the position or want to register your interest, hit apply below. We'd love to hear from you!TPBN1_UKTJ

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 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.

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.

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.