Machine Learning Engineer

Expedia Group
City of London
2 days ago
Create job alert

Machine Learning Engineer III


Expedia Group brands power global travel for everyone, everywhere. We design cutting-edge tech to make travel smoother and more memorable, and we create groundbreaking solutions for our partners. Our diverse, vibrant, and welcoming community is essential in driving our success.


Why Join Us?


To shape the future of travel, people must come first. Guided by our Values and Leadership Agreements, we foster an open culture where everyone belongs, differences are celebrated and know that when one of us wins, we all win.


We provide a full benefits package, including exciting travel perks, generous time-off, parental leave, a flexible work model (with some pretty cool offices), and career development resources, all to fuel our employees' passion for travel and ensure a rewarding career journey. We’re building a more open world. Join us.


Expedia Group’s Advertising Engineering team is dedicated to building innovative solutions that empower travel advertisers to connect with millions of travelers worldwide. Our platform enables brands to leverage Expedia’s global network of leading travel brands and sites, offering a diverse portfolio of advertising and sponsorship opportunities. With over 200 branded sites in 75 countries and 35 languages, we help advertisers reach 112 million monthly unique visitors.


As a Machine Learning Engineer III on this team, you will design and implement scalable machine learning systems that optimise ad selection, campaign performance, and creative personalisation at a global scale. You will work in a highly collaborative environment with ML scientists and software engineers to deliver impactful solutions for the advertising domain.


In this role, you will:


  • Design, implement, and maintain large-scale machine learning pipelines for advertising use cases, including feature engineering, model training, validation, and deployment.
  • Build real-time and batch data processing systems to support ad targeting, campaign optimisation, and experimentation.
  • Collaborate with ML scientists and software engineers to integrate ML models into production systems and deliver measurable business impact.
  • Develop APIs and services that enable ML-driven advertising solutions across Expedia Group’s global platform.
  • Optimize Spark-based applications for large-scale data processing and ensure system reliability and performance.
  • Implement strategies for training models on massive datasets using distributed computing and GPU acceleration.
  • Contribute to design discussions and code reviews, ensuring best practices in ML engineering and software development.
  • Mentor junior engineers and share knowledge within the team and broader engineering community.
  • Continuously explore new technologies and methodologies to improve ML systems and advertising solutions.
  • Participate in a community of practice to share and gain knowledge across the organization.


Required qualifications:


  • Bachelor’s or Master’s degree in Computer Science, Engineering, or equivalent experience.
  • 5+ years of professional experience with a Bachelor’s degree OR 3+ years with a Master’s degree.
  • Proven experience building and deploying ML pipelines in production environments.
  • Strong programming skills in Python and at least one other language (e.g., Scala or Java).
  • Expertise in Spark and distributed data processing frameworks.
  • Proficiency with ML libraries such as PyTorch and TensorFlow.
  • Experience with cloud platforms (AWS, EMR, Kubernetes, Docker) and ML platforms (Databricks, SageMaker).
  • Familiarity with workflow management tools (e.g., Airflow).
  • Strong understanding of software design principles, data structures, and design patterns.
  • Ability to debug, test, and monitor complex systems effectively.


Preferred: qualifications:


  • Experience with real-time applications and streaming architectures.
  • Knowledge of advertising technology, e-commerce, or travel industry.
  • Hands-on experience with large-scale model training and optimization using GPUs or distributed systems.

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer - London

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

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.