Device Simulation and Design Engineer

Ipswich
9 months ago
Applications closed

Related Jobs

View all jobs

Computer Vision Physicist / Engineer

Technical Solutions Engineer – Deep-Tech AI (RetailTech / Computer Vision)

Technical Solutions Engineer – Deep-Tech AI (RetailTech / Computer Vision)

Benefit Risk Management Center of Excellence Data Scientist

Benefit Risk Management Center of Excellence Data Scientist

Computer Vision & SDK Development Lead

Device Simulation and Design Engineer
Ipswich
£70,000 per annum

A global technology leader is expanding their R&D capability and seeking a Device Simulation and Design Engineer to join their team in Ipswich. This is a brand-new opportunity to shape and develop next-generation III-V photonic devices through cutting-edge simulation, design, and automation techniques.

As part of a world-class team of engineers, you'll develop and refine optical simulation tools, create scalable and automated design flows, and use your deep understanding of optoelectronic device physics to deliver high-performance components ready for fabrication and experimental testing.

Key Responsibilities

Develop advanced simulation tools for III-V photonic devices using techniques including FDTD, FEM, machine learning, and inverse design.
Contribute to the design and development of both active and passive components.
Automate simulation workflows and data analysis to optimise design iterations.
Benchmark, document, and deliver tools for internal use across design teams.
Collaborate with fabrication and test teams to ensure seamless end-to-end development.
Analyse experimental test results to calibrate and improve model accuracy.
Produce technical reports and present findings to stakeholders. About You

You'll bring a proven background in optoelectronic design, a passion for innovation, and a collaborative mindset.

Essential:

PhD in Physics, Electronic Engineering, or a related field.
Minimum 5 years' experience in photonics, with strong III-V device simulation expertise.
Hands-on knowledge of tools such as COMSOL, VPI, FDTD, FEM, and multiphysics simulation environments.
Proficient in Python or similar for automation, modelling and analysis.
Experience with machine learning or inverse design methods.
Strong data analysis and communication skills, including technical reporting.Desirable:

Familiarity with characterisation and test of photonic devices.
Experience in the design of lasers, modulators, waveguides and couplers.
A track record of publications or conference participation. Benefits

Up to 33 days annual leave including public holidays
Company pension scheme
Private medical insurance and healthcare support
Life assurance
Employee assistance programme
Cycle to work scheme
Professional development time and support
Regular company events and team activities

If you're ready to influence the future of optoelectronic design, please click "Apply Now

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 Jobs

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

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.

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.

🍪 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.