Part Qualified / Newly Qualified High-Tech Patent Attorney

Dawn Ellmore Employment Agency
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Science Placement Programme

Data Science Placement Programme

Data Scientist-Manager

Senior Data Scientist - Game Analytics

AIML - Machine Learning Research (Speech Translation)

Senior Data Scientist - Live Service Integrity

Part Qualified / Newly Qualified High-Tech Patent Attorney

We are pleased to be assisting a highly reputable firm as they search for a part qualified or newly qualified patent attorney to join their leading high-tech team in London.

The firm has a diverse client base comprising many well-known names, and if successful you can expect a varied caseload covering a range of areas including artificial intelligence (machine learning), blockchain, electronics, semiconductors and telecommunications. As you would expect with such an impressive client list, this role will involve some high quality original drafting and prosecution work, but in addition to this there is a large amount of contentious and opposition work that you will have the opportunity to assist the Partners with.

If you are successful, you will be welcomed into a friendly and hardworking atmosphere, where a healthy work/life balance is promoted. The firm operates a transparent framework for promotion, meaning that you will know what is required at all times to realise your ambitions.

All in all, this represents a fantastic opportunity for an ambitious patent attorney looking to give their career a boost and join a firm where their hard work will be rewarded.

#J-18808-Ljbffr

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.