Machine Learning Engineer III

Expedia Group
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist - Inside IR35 - Hybrid

Data Engineer - (Python, SQL, Machine Learning) - Robotics

Data Engineer - (Python, SQL, Machine Learning) - Robotics

Data Engineer - Python, SQL, Machine Learning - Robotics

Data Engineer - (Python, SQL, Machine Learning) - Robotics

Data Engineer Python SQL AWS

Machine Learning Engineer:

Are you fascinated by machine learning and building robust machine learning pipelines which process massive amounts of data at scale and speed to provide crucial insights to the end consumers?

This is exactly what we, the Machine Learning Engineering group in Expedia, do.  Our mission is to partner with our Machine Learning Science counterparts to use AI/ML to collaboratively transform Expedia’s data assets into intelligent and real-time insights to support a variety of applications which are used by 1000+ market managers, analysts, our supply partners, and our travelers. Our work spans across a variety of datasets and ML models and across a diverse technology stack ranging from Spark, Sagemaker, Airflow, Databricks, Kubernetes, AWS and much more! 

What you will do:

  • Work in a cross-functional team of Machine Learning engineers and Machine Learning Science to design and code large scale batch and real-time ML pipelines

  • Prototype creative solutions quickly by developing minimum viable products and work with seniors and peers in crafting and implementing the technical vision for the team

  • Communicate and work with geographically distributed cross functional teams 

  • Participate in code reviews to assess overall code quality and flexibility 

  • Resolve problems and roadblocks as they occur with peers and help unblock junior members of the team. Follow through on details and drive issues to closure

  • Define, develop, and maintain artifacts like technical design or partner documentation

  • Drive for continuous improvement within an agile development team

  • Participate in user story creation in collaboration with the team

  • Support and troubleshoot data and/or system issues as needed

Who you are:

  • Degree in software engineering, computer science or a similar field.

  • Comfortable programming in Python and Scala (or Java)

  • Knowledgeable in Big Data technologies, in particular Hadoop, Hive, and Spark.

  • Experience in building real-time applications, preferably in Spark

  • Good understanding of machine learning pipelines and machine learning frameworks such as TensorFlow and Pytorch

  • Familiar with cloud services (e.g., AWS) and workflow orchestration tools (e.g., Airflow)

  • Experience working with Agile/Scrum methodologies.

  • Familiar with the e-commerce or travel industry.

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 Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.