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⚡ Source: ReedRef: 56945269

Software Engineer, Advanced Composites

IT Search & Select·Islington, London·Posted 4 days ago
💰 £50-80k/year
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Job description

Original text imported from Reed

Our client, a leader in advanced composite manufacturing, automated lay-up systems, and next-generation materials engineering, is expanding its London engineering team. They are seeking talented Software Engineers with experience in composites, manufacturing automation, or materials-driven engineering environments.

This role sits at the intersection of software, robotics, and high-performance composite structures, contributing to systems used across aerospace, automotive, defence, and clean-tech sectors.

The Role

You will develop control systems, automation software, simulation tools, and data-driven manufacturing platforms used in composite production. Working closely with mechanical, materials, and process engineers, you’ll deliver robust, high-performance software for real-world manufacturing environments.

Key Responsibilities
  • Develop and maintain software for automated composite manufacturing systems
  • Build control algorithms for robotic lay-up, curing, trimming, and inspection processes
  • Integrate software with sensors, machine vision, PLCs, and industrial hardware
  • Create simulation, modelling, and optimisation tools for composite structures
  • Support R&D projects involving new materials, resins, and fibre-placement technologies
  • Collaborate with cross-functional teams to deliver customer-specific solutions
  • Contribute to testing, validation, and continuous improvement of production systems
Skills & ExperienceEssential
  • Strong programming skills in C++, Python, or C#
  • Experience in manufacturing systems, automation, robotics, or industrial software
  • Understanding of composite materials, lay-up processes, or materials engineering
  • Familiarity with control systems, real-time software, or embedded development
  • Ability to work on customer-driven engineering projects
Desirable
  • Experience with PLC programming
  • Knowledge of finite element analysis, simulation, or optimisation tools
  • Exposure to machine vision, sensor fusion, or data acquisition systems
  • Background in aerospace, automotive, motorsport, or advanced manufacturing
What Our Client Offers
  • Work on cutting-edge composite manufacturing innovations
  • A collaborative, engineering-focused culture
  • Opportunities to influence next-generation production systems
  • Career progression across software, automation, and R&D
  • Competitive salary + benefits


SpeedCV AI

Key skills

AI-extracted from the job advert

Must-have skills
C++PythonC#Manufacturing automation softwareControl systems developmentReal-time or embedded software developmentComposite materials or lay-up process knowledge
Nice-to-have
PLC programmingFinite element analysisMachine visionSensor fusionData acquisition systemsAerospace or automotive domain experience
Soft skills
Cross-functional collaborationCustomer-driven deliveryAutonomyContinuous improvement mindsetAdaptability
SpeedCV AI

Application advice

5 AI-generated recommendations to maximise your chances.

1

⭐ Lead your CV personal statement with C++/Python and composite manufacturing automation — the advert lists these as the top essential skills and ATS will filter on them first.

2

📊 Quantify your automation work: e.g. 'Developed control algorithms for a 6-axis robotic lay-up cell, reducing cycle time by 22% across 3 production lines'.

3

🎯 Call out any aerospace, automotive, motorsport, or defence project context explicitly — the advert names all four sectors and hiring managers will scan for domain match.

4

🤝 Highlight cross-functional collaboration with mechanical and process engineers, as the role explicitly requires working alongside non-software disciplines on customer-specific solutions.

5

🌐 If you have PLC programming or FEA/simulation experience, give each its own bullet — these are listed as desirable and will differentiate your application from pure software candidates.

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Suggested CV bullets

3 bullets our AI drafted for this specific advert, mirroring its ATS keywords.

How to tailor your CV

Add these 3 bullets under your most recent experience:

  • Developed real-time C++ control software for a 6-axis robotic composite lay-up cell, cutting average cycle time by 18% across 4 production programmes in the aerospace sector.
  • Integrated machine vision inspection pipeline with PLC hardware and sensor fusion modules, achieving 96% defect-detection accuracy on carbon fibre panel production lines.
  • Built a Python-based simulation and optimisation tool for curing process parameters, reducing resin waste by 12% and shortening qualification cycles from 6 weeks to 4 weeks.

Free to copy — tailoring requires a 30-sec CV upload.

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Dear Hiring Manager,

Advanced composite manufacturing sits at a genuinely rare intersection of software and materials engineering — and the Software Engineer role at your client's London team is precisely where I want to apply my C++ and Python control-systems experience. Having worked on automation software for industrial manufacturing environments, I understand the rigour required to deliver real-time, customer-specific solutions that perform reliably on the shop floor.

My background in developing control algorithms and integrating software with industrial hardware — including PLC interfaces and sensor pipelines — maps directly to the robotic lay-up, curing, and inspection systems described in this role. I have collaborated closely with mechanical and process engineers to validate software against physical constraints, and I have contributed to simulation tooling used to optimise production parameters before live deployment.

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Interview questions

10 questions generated from this advert.

Technical

  • Walk us through how you would architect a real-time control system for a robotic composite lay-up cell using C++ — what concurrency model would you choose and why?
  • How have you integrated software with PLCs or industrial hardware? What communication protocols did you use and what were the key reliability challenges?
  • Describe your approach to building a simulation or optimisation tool for a manufacturing process — what inputs, solvers, and validation methods would you employ?
  • How would you implement a machine vision pipeline for automated inspection of composite panels, and how would you handle false-positive defect detection?
  • What strategies do you use to ensure software safety and determinism in embedded or real-time manufacturing environments?

Behavioural

  • Tell me about a time you had to deliver a software solution under tight constraints imposed by a customer-specific engineering requirement.
  • Describe a situation where you collaborated closely with mechanical or materials engineers — how did you bridge the gap between software and physical domain knowledge?
  • Give an example of when you identified a significant flaw in a production system during testing and how you resolved it before deployment.
  • Tell me about a time you had to learn a new domain (e.g. composites, robotics, or a new programming language) quickly to meet project demands.
  • Describe a continuous improvement initiative you led or contributed to on a manufacturing or automation software project — what was the measurable outcome?
SpeedCV AINEW

STAR answer examples

Model answers using the Situation-Task-Action-Result framework. Adapt to your own experience.

1Question

Tell me about a time you had to deliver a software solution under tight constraints imposed by a customer-specific engineering requirement.

Situation: A motorsport client required a bespoke data-logging module integrated into an existing composite inspection rig within five weeks — half the originally scoped timeline. Task: I was the sole software engineer responsible for design, integration, and sign-off. Action: I prioritised a minimal viable interface first, negotiating with the client to defer three non-critical reporting features to a phase-two release. I wrote the C++ acquisition layer in two weeks, then spent the remaining time on hardware integration with the PLC and validation testing. Result: The module shipped on time, captured 100% of required sensor channels at 1 kHz, and the deferred features were delivered four weeks later with zero rework — the client extended the contract for a second rig.
2Question

Describe a situation where you collaborated closely with mechanical or materials engineers — how did you bridge the gap between software and physical domain knowledge?

Situation: During an automated curing system project, the materials team flagged that my temperature-ramp control algorithm was producing 3°C overshoot, risking resin degradation in high-value carbon fibre panels. Task: I needed to retune the PID controller without access to a formal thermal model. Action: I spent two days shadowing the materials engineers during manual curing runs, logging thermocouple data to build an empirical transfer function in Python. I then implemented a feedforward compensation term and validated it across 12 test cycles with the team present. Result: Overshoot dropped to under 0.8°C, scrap rate on that panel grade fell from 7% to under 1%, and the materials team adopted the logging script as a standard process tool.

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