Engineering Manager - Marine Systems

T2M Resourcing
Milton Keynes
1 year ago
Applications closed

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Principal Systems Engineer Competitive Salary Milton Keynes - Hybrid T2M Resourcing are working with a growing technology and manufacturing business to recruit a Principal Systems Engineer to lead systems requirements, design and architecture, testing, and overall V-Cycle activities. As a Principal Systems Engineer you will specialise in managing systems activities and working with multi-discipline teams in software and hardware development. Duties of the Principal Systems Engineer: Leading systems activities across the V-Cycle alongside multi-discipline teams. Develop and manage requirements from product conception to delivery. Deliver functional safety activities to ISO26262 standards. Delivering test cases and plans alongside Software Test Engineers. System architecture design utilising SysML. Mentor and lead engineers during technical projects. Complete and oversee systems tasks such as FMEA and HAZOP and HARA. Support software team with development of models using Simulink/Matlab. Requirements to be successful as a Principal Systems Engineer: Degree level qualification in Systems Engineer, Electronics Engineering, Mechanical Engineering or similar field. Able to lead by example. Knowledge of Functional Safety and ISO26262 standards. Testing experience such as Software-in-loop and Hardware-in-loop. Specialist understanding of model-based systems engineering (MBSE). Clear communicator. Experience of automotive/aerospace systems engineering processes. Working knowledge of hardware, systems, and software development tools, such as: SysML MATLAB/Simulink Embedded Coders DOORS ~ Strong problem-solving skills. This role is based in Milton Keynes and can be completed as a hybrid role with up to 2 days a week from home. Due to high to the high volume of applications we are receiving we are unable to respond to each candidate personally. If you have not heard from us within 14 days, unfortunately your application will not have been successful.

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Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

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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.

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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.

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