Principal Machine Learning Engineer

Sage
London
1 year ago
Applications closed

Related Jobs

View all jobs

Principal Machine Learning Engineer

Principal Machine Learning Engineer

Data Science Principal

Principal Data Engineer

Principal Data Scientist

Principal Data Scientist

Job Title

Principal Machine Learning Engineer

Job Description

Sage AI is a nimble team within Sage, building innovative services and solutions using generative AI and machine learning to turbocharge our users' productivity. The Sage AI team builds capabilities to help businesses make better decisions through data-powered automation and insights.

We are currently hiring a Principal Machine Learning Engineer to help us build machine learning solutions that will provide insights to empower businesses and help them succeed. As a part of our cross-functional team including data scientists and engineers you will help steer the direction of the entire company's Artificial Intelligence and Machine Learning initiatives.

This is a hybrid role - three days per week in our London office.

If you share our excitement for applying artificial intelligence and machine learning, value a culture of continuous improvement and learning and are excited about working with cutting edge technologies, apply today!

You will:
•Design and implement product features and services that use AI and ML to augment and simplify our customers' workflows
•Develop our internal ML platform to support our machine learning systems and our own efficiency
•Monitor and optimize the quality and performance of our models, services, and tools
•Collaborate with our AI Platform team to extend the capabilities of our machine learning platform
•Design and write robust production-quality code to support our machine learning systems
•Build and operate pipelines for accessing and enriching data for machine learning
•Train, tune, and ship models
•Mentor other ML engineers, software engineers, and data scientists in best practices
•Work with product managers and data scientists to translate product/business problems into tractable machine learning solutions

Key Responsibilities

You have:
•Keen interest in artificial intelligence and machine learning and extensive practical experience with it
•Expert knowledge and experience with relevant programming languages (incl. Python), frameworks (incl. Pycharm, OpenAI, HuggingFace, Spark, Azure, AWS)
•Extensive experience with cloud environments (AWS, Azure, GCP)
•Ability to write highly performant code working with big data
•Bachelor's degree, preferably in a field that strongly uses data science / machine learning techniques (e.g. computer science/engineering, statistics, applied math)
•Fluency in data fundamentals: SQL, data manipulation using a procedural language, statistics, experimentation, and predictive modelling
•Strong quantitative and analytical skills with significant experience with data science tools
•Ability to communicate complex ideas in machine learning to non-technical stakeholders

You may have:
•Experience with one or more ML Ops frameworks - MLFlow, Kubeflow, Azure ML, Sagemaker
•Strong theoretical foundations in linear algebra, probability theory, or optimization
•Experience and training in finance and operations domains
•Deep experience with ML approaches: deep learning, generative AI, large language models, logistic regression, gradient descent
•Experience wrangling complex and diverse data to solve real-world problems

What's it like to work here:

You will have an opportunity to work in an environment where ML engineering is central to what we do. The products we build are breaking new ground, and we have a focus on providing the best environment to allow you to do what you do best - solve problems, collaborate with your team and push first class software. Our distributed team is spread across multiple continents, we promote an open diverse environment, encourage contributions to open-source software and invest heavily in our staff. Our team is talented, capable, and inclusive. We know that great things can only be done with great teams and look forward to continuing this direction.

Function

Product

Country

United Kingdom

Office Location

London

Work Place type

Hybrid

Advert

Working at Sage means you're supporting millions of small and medium sized businesses globally with technology to work faster and smarter. We leverage the future of AI, meaning business owners spend less time doing routine tasks, like entering invoices and generating reports, and more time pursuing their ambitions.

Our colleagues are the best of the best. It's why we were awarded 2024 Best Places to Work by Glassdoor. Because to achieve extraordinary outcomes, we need extraordinary teams. This means infusing Sage with people who knock down barriers, continuously innovate, and want to experience their potential.
Learn more about working at Sage: sage.com/en-gb/company/careers/working-at-sage/
Watch a video about our culture: youtube.com/watch?v=qIoiCpZH-QE

We celebrate individuality and welcome you to join us if you embrace all backgrounds, identities, beliefs, and ways of working. If you need support applying, reach out at .
Learn more about DEI at Sage: sage.com/en-gb/company/careers/diversity-equity-and-inclusion/

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.

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.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.