Principal Firmware Engineer

Cambridge
10 months ago
Applications closed

Related Jobs

View all jobs

Principal Data Analyst

Principal Machine Learning Engineer - Production Systems

Principal Data Engineer

Principal Data Engineer...

Principal Data Engineer...

Principal Data Engineer

Developing embedded firmware for ARM-based microcontrollers

An excellent opportunity for a skilled firmware developer to make an impact on a growing Cambridge company.

Working with ARM-based microcontrollers, your expertise in embedded software engineering will be crucial to expand the capability of the group and support the evolution of their sensor technology.

Together with colleagues in hardware design and data science, you would be working on firmware for applications, incorporating new features for low level test data, and the implementation, characterisation and analysis of new algorithms. You will be used to working to an ISO9001 framework and ideally you will also have experience with medical or automotive industry standards such as IEC 62304 and ISO26262.

Skills and experience you will need:

  • A good engineering or scientific degree from a well-respected university

  • Experience with developing bare metal, real-time firmware in C for ASICS or ARM-based microcontrollers such as STM32 and EFM32, and using tools such as: Keil, IAR, STM32CubeMX and Eclipse/gnu

  • Source code management systems such as Git and Perforce

  • Familiar with automated testing of firmware builds, as well as regression testing

  • Debugging using JTAG interface adapters and testing using oscilloscopes and logic analysers

  • Python

    Other advantageous skills include:

  • Development of Windows GUI applications in C#

  • Experience with Continuous Integration (CI) systems

  • Experience of digital or analogue hardware design

    You will also need full rights to work in the UK without time limit or sponsorship.

    Our client offers a friendly work environment that encourages your professional growth, along with a competitive remuneration package. They are based in modern facilities with free parking and good links to Cambridge city centre and London.

    Keywords: Firmware, Cambridge, ARM microcontrollers, STM32, EFM32, Python, C#, Principal, Senior, Bare Metal, Embedded, C, ASIC

    Another top job from ECM, the high-tech recruitment experts.

    Even if this job's not quite right, do contact us now - we may well have the ideal job for you. To discuss your requirements call (phone number removed) or email your CV. We will always ask before forwarding your CV.

    Please apply (quoting ref: CV27311) only if you are eligible to live and work in the UK. By submitting your details you certify that the information you provide is accurate

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.