Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Sr. Machine Learning Engineer

Adobe
Edinburgh
1 year ago
Applications closed

Related Jobs

View all jobs

Senior DevOps Engineer, Machine Learning

Sr. Data Scientist, GenAI Algorithms (Based in Dubai)

Sr. Data Scientist, GenAI Algorithms (Based in Dubai)

AI & Data Scientist

Legal Counsel – AI and Machine Learning

Associate Director, AI Data Scientist

Our Company

Changing the world through digital experiences is what Adobe’s all about. We give everyone—from emerging artists to global brands—everything they need to design and deliver exceptional digital experiences! We’re passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen. 

We’re on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours!

The Opportunity

Adobe is looking for a Machine Learning Engineer from mid to senior level to use Generative AI and Machine Learning techniques to help Adobe better understand, lead, and optimize how we develop our world class applications. Partnering with Adobe development teams across the company, the successful candidate will be building models that allow us to both understand and improve the very nature of software development

What you'll Do 

Partner with development teams across the company to collate appropriate datasets

Design and build applications powered by generative AI, that allow us to mine insights from these datasets that improve the overall engineering culture across the company.

Engage in the product lifecycle, design, deployment, and production operations.

Provide technical leadership in everything from architectural design and technology choices to holistic evaluation of ML models.

What you need to succeed

The ideal candidate will have the following background:

PhD or MS degree in Computer Science, Data Science or related field required.

5 to 10 years of applied research experience in software industry/academic research withexperience in developing,evaluating ML models, and deploying models into production.

Deep understanding of statistical modelling, machine learning, or analytics concepts, and a track record of solving problems with these methods; ability to quickly learn new skills and work in a fast-paced team.

Proficient in one or more programming languages such as Python, Scala, Java, SQL. Familiarity with cloud development on Azure/AWS.

Fluent in at least one deep learning framework such as TensorFlow or PyTorch.

Experience with LLMs and emerging area of prompt-engineering.

Recognized as a technical leader in related domain.

Experience working with both research and product teams.

Excellent problem-solving and analytic skills

Excellent communication and relationship building skills.

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 an AI CV that Beats ATS (UK examples)

Writing an AI CV for the UK market is about clarity, credibility, and alignment. Recruiters spend seconds scanning the top third of your CV, while Applicant Tracking Systems (ATS) check for relevant skills & recent impact. Your goal is to make both happy without gimmicks: plain structure, sharp evidence, and links that prove you can ship to production. This guide shows you exactly how to do that. You’ll get a clean CV anatomy, a phrase bank for measurable bullets, GitHub & portfolio tips, and three copy-ready UK examples (junior, mid, research). Paste the structure, replace the details, and tailor to each job ad.

AI Recruitment Trends 2025 (UK): What Job Seekers Must Know About Today’s Hiring Process

Summary: UK AI hiring has shifted from titles & puzzle rounds to skills, portfolios, evals, safety, governance & measurable business impact. This guide explains what’s changed, what to expect in interviews, and how to prepare—especially for LLM application, MLOps/platform, data science, AI product & safety roles. Who this is for: AI/ML engineers, LLM engineers, data scientists, MLOps/platform engineers, AI product managers, applied researchers & safety/governance specialists targeting roles in the UK.

Why AI Careers in the UK Are Becoming More Multidisciplinary

Artificial intelligence is no longer a single-discipline pursuit. In the UK, employers increasingly want talent that can code and communicate, model and manage risk, experiment and empathise. That shift is reshaping job descriptions, training pathways & career progression. AI is touching regulated sectors, sensitive user journeys & public services — so the work now sits at the crossroads of computer science, law, ethics, psychology, linguistics & design. This isn’t a buzzword-driven change. It’s happening because real systems are deployed in the wild where people have rights, needs, habits & constraints. As models move from lab demos to products that diagnose, advise, detect fraud, personalise education or generate media, teams must align performance with accountability, safety & usability. The UK’s maturing AI ecosystem — from startups to FTSE 100s, consultancies, the public sector & universities — is responding by hiring multidisciplinary teams who can anticipate social impact as confidently as they ship features. Below, we unpack the forces behind this change, spotlight five disciplines now fused with AI roles, show what it means for UK job-seekers & employers, and map practical steps to future-proof your CV.