Principal Analog IC Design Engineer – Computing – Remote

IC Resources
Leeds
9 months ago
Applications closed

Related Jobs

View all jobs

Principal Machine Learning Engineer - Production Systems

Principal Data Scientist: Scale ML for Audiences (Hybrid)

Principal Data Scientist London, United Kingdom

Principal Data Scientist: ML Leader, Mentor, Hybrid Role

Principal Data Scientist

Principal Machine Learning Engineer

This is an opportunity for a Principal Analog IC Design Engineer to join a progressive startup company working on computing applications. This role offers very flexible remote working, with visits to the London or Cambridge office as and when required.


As the Principal Analog IC Design Engineer, you will be responsible for the design, simulation and layout of Analog circuits ensuring their functionality, performance, and reliability. Collaboration with cross-functional teams and contributing to the technical roadmap will also be an essential part of this role.


Industry degree qualified the successful Principal Analog IC Design Engineer will have a minimum of 8 years’ experience and strong expertise in designing and implementing Analog circuits from scratch, including experience in advanced process nodes down to 7nm, clock/data recovery, resonators, crystal oscillators, VCOs and PLLs.


Experience is required in some or all of the following:


  • Experience working in a fast-paced environment, ideally a startup.
  • Proficiency in using industry-standard EDA tools.
  • Experience with analog design techniques, including layout, parasitic extraction, and noise analysis.
  • Solid knowledge of semiconductor device physics and fabrication processes.
  • Familiarity with mixed-signal design and verification is a plus.


You will have excellent problem-solving skills, with the ability to analyse and resolve complex circuit design issues. Excellent communication skills are required along with the ability to effectively collaborate with cross-functional teams.


This role can be based in the US (if you have the right to work there).


Contact Caroline at IC Resources today to apply!

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.

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

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.

How to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

Maths for AI Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.