Do you want to power the future of travel? Then come and make a positive impact, strengthen connections, and bridge divides across the world, at Expedia Group. You can help us bring people together through travel technology, while jump-starting your career in Machine Learning Science.

Travel is so much more than simply reaching your destination. Along your journey you will make an immediate impact on reimagining the way people search for travel and invent new techniques to help travelers find the right pathways through millions of possibilities. From prototyping new ML models with A/B testing to applying new techniques to services that run tens of thousands of requests per second, there’s no shortage of opportunities to innovate at Expedia Group!

We are hiring across multiple teams, therefore the role may differ dependent on this. Sound interesting? Then check out what our current graduates are working on, below!

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Srinivas Billa, Machine Learning Scientist in London

Tell us about your team

I work in the Traveller Voice and Content team, we mainly work on NLP and Computer Vision applications within EG. 

Tell us about your role

Since I joined the team last year, I mainly work with llms (ChatGPT, llama, mistral…) on intakes from the content team where llms are used to make new features and solve tasks. For example, my most recent project was to generate review summaries for the amenity modal which provides the traveller with a quick overview without having to read hundreds of reviews. 

What technologies and languages do you work with?

Python, SQL, Spark, Databricks, LLMs 

What skillsets do you think are required to be successful in your role?

I knew how to code in python, pyspark would be immensely helpful if I knew it however I learnt pyspark after I joined EG so its not too difficult. But Those two are the ones I use on a daily basis 

Almost immediately after onboarding I was given projects, I had to interact with stakeholders, manage expectations and timelines. All of which you never really learn elsewhere in academia. 

What is your favorite thing about Expedia Group

I would say the visible immediate impact that my work has. As I mentioned earlier the review summaries feature was pushed into live production and I could show everyone who asked “what do you do as an MLS at EG?” 

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Alex Manlove, Machine Learning Scientist in London

Tell us about your team

I work in the Reinforcement Learning team. We build and maintain the AdaptEx platform, which supports other teams and stakeholders within the business to dynamically optimise traveller experiences, such as website layouts, images or content designs. This is achieved through clever use of reinforcement learning algorithms that allow for the platform to learn and reactively adapt to traveller behaviour in real time.

Tell us about your role

As a Machine Learning Scientist, my role is primarily concerned with research in order to understand the algorithms that make all this possible. This process involves reading literature, discussing theory with colleagues and running experiments. It’s not always obvious how to translate abstract theoretical concepts into practical real-world applications. Therefore a key part of the role also involves interfacing with stakeholders to explore how these academic theories and general-purpose technologies can be mapped on to their specific business needs, in order to solve the task at hand.

What technologies and languages do you work with?

Python, Scala, TensorFlow, PyTorch, Git, Databricks.

What skillsets do you think are required to be successful in your role?

The scientists in this team benefit from a solid knowledge of machine learning theory. In particular reinforcement learning and Bayesian statistics are two key areas. Aside from this, it’s also important to have good technical skills, with knowledge of Python, Scala and distributed computing. Prior to working at EG I was comfortable hacking together code for notebooks or smaller projects, but since then I’ve learned from my colleagues about how to write clean, well-structured implementations that are easy to maintain and extend.

What is the biggest difference between being an intern and being a full-time employee?

The internship was a great opportunity for me to familiarise myself with Expedia Group’s internal tools and processes, as well as the general company culture. At that time my knowledge was still developing and my contributions were smaller. As a full-time employee I’m now much more confident and trusted to work on larger projects where I can take full ownership of my work.

What is your favorite thing about working at Expedia Group?

My two favourite things are the people and the culture. My colleagues are highly knowledgeable and always willing to help. The culture in the office is collaborative rather than competitive. We all work together and support eachother to achieve our goals. I learn something new every single day, whether that be through reading papers together, discussions about machine learning theory or feedback comments on pull requests. The culture is exceptionally open and inclusive. I feel comfortable asking questions and sharing my ideas. The flexible hybrid work policy means I feel trusted to manage my own time and work in a way that suits me best. I feel equipped with the tools and guidance I need to grow and achieve my best work.

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Joanna Krodkiewska, Machine Learning Scientist in London

Tell us about your team

I’m part of the AI and Data Science organization which works on building, deploying, and maintaining machine learning models with various applications across Expedia Group (EG), such as hotel search rank, image rank for properties’ images and fraud detection. The team I’m working in is Content AI & ML. The team uses machine learning to build models around the content display on EG websites. The three main areas are: computer vision (anything to do with images displayed on the websites), NLP (working with text data) and structured content such as hotel amenities.

Tell us about your role

I’m currently working with structured data only but I’m looking to get involved with NLP problems in the future as well. The typical tasks I’m working on involve data cleaning, feature engineering and evaluating model performance as well as working on building and improving data pipelines.

What skillsets do you think are required to be successful in your role?

I would say you need a theoretical understanding of machine learning and various classification and regression algorithms. A second, and equally important, component is coding skills as well as having experience implementing some simple ML models.

What technologies and languages do you work with?

I  work mostly with Spark, Python and Scala.

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Jack Pennington, Machine Learning Scientist in London

Tell us about your team

“I’m in the Personalization & Recommendations Team and within that I’m on the Intents team. We work on predicting user intentions on our platform. These could be:

Are they going to book? What line of business are they interested in? Location? Hotel vs. Vacation Rental? By the beach? Near a museum? Using this information we can personalise their UX, therefore increasing conversions and increasing revenue.”

Tell us about your role

“I’m a Machine Learning Scientist, and currently I’m working on a project called Digital Experience Score (Struggle Score) which is an AI-based approach to measure and detect friction and task success in all EG user experiences. Alongside that I’m working on refactoring our training pipeline for the intent prediction, and creating a data extraction pipeline to extract data from clickstream. I’m also working with the Clickstream product team to get telemetry events added to clickstream.”

What skillsets do you think are required to be successful in your role?

“A strong mathematical background, knowledge of Machine Learning, programming fundamentals, problem solving and taking initiative.”

What technologies and languages do you work with?

“Languages: Scala, Python, SQL

Frameworks/Libraries: Spark (Scala/ PySpark), Tensorflow, Python Packages (Numpy, Pandas, Matplotlib etc.)

Tools: AWS, Databricks, Git”

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Kaia Bonnet, Machine Learning Scientist in Geneva

Tell us about your team

“We are in the Data Science and Analytics organization. The Scout team is working on machine learning algorithms to help our supply partners and support market managers.”

Tell us about your role

“Our role is to develop and maintain machine learning algorithms that respond to business needs. We are working closely with engineers and stakeholders (mostly market management teams). It’s important we understand the business need and help to translate this into a machine learning problem and propose a solution. A lot of the work is also related to tweaking existing algorithms for new requirements and fixing bugs that may arise.”

What skillsets do you think are required to be successful in your role?

“Have a strong foundation in machine learning to understand the models and propose the best solutions. Previous experience with coding to be able to develop and maintain the algorithms. Good communication skills to understand the need of stakeholders and explain the technical solutions and possible limitations. Someone who likes to learn because this field is evolving fast, so there will always be a need to learn new tools and more theory.”

What technologies and languages do you work with?

“Python and SQL. Distributed environments like Spark. The cloud services we use are Databricks and Qubole.”

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Machine Learning Scientist Graduates do not join our typical Graduate Program format, due to their level of technical expertise. Instead they join our 1st Year Experience, which includes access to tech learning communities, panel discussions, and development opportunities through skill-builder courses.

We’re looking for outstanding talent to join us on our mission to power global travel for everyone, everywhere. Take a look at our latest Emerging Talent & Careers opportunities here.