Senior Machine Learning Scientist

NLP PEOPLE
London
1 month ago
Create job alert

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 global hybrid work setup (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.

Introduction to the Team

Expedia Product & Technology builds innovative products, services, and tools to deliver high-quality experiences for travelers, partners, and our employees. A singular technology platform powered by data and machine learning provides secure, differentiated, and personalized experiences for the traveler and our partners that drive loyalty and customer satisfaction.

This position sits with our growing Traveler Voice and Content team. This position focuses on the development of Generative AI-powered Travel Discovery and Shopping experiences. The ideal candidate for this position is a true deep learning expert with strong knowledge and background in both recommender systems as well as Generative AI. Innovation and developing cutting-edge technology as well as implementing industry-leading solutions are key responsibilities of this role. The machine learning models and algorithms you develop will improve the experiences of millions of travelers and travel partners each year.

What You’ll Do:

  1. Conduct research and development in the following areas: Large Language Models (LLMs), Generative Retrieval, Sequential Recommender models, Mixture of Expert models, AI Agents and Multi-modal foundational models.
  2. Develop Deep Learning AI models with superior performance to power Expedia’s Travel platform.
  3. Conduct analysis of the model performance using data collected from the field.
  4. Optimize existing models for improved accuracy and better performance.
  5. Produce novel insights with machine learning to advise company strategy.
  6. Communicate sophisticated concepts and the results of the analyses in a clear and effective manner.

Who You Are:

  1. You have a Master’s Degree or Ph.D. in Computer Science, Statistics, Math, Engineering or equivalent experience.
  2. You have experience in Generative AI and/or Recommender Systems, Deep Learning, Data/Machine Learning Science.
  3. You possess expert knowledge of Python programming language.
  4. You have a strong knowledge and expertise in PyTorch and/or TensorFlow.
  5. You have proven knowledge of processing and analyzing large scale data volumes, structured and unstructured data and near real-time throughput.

Accommodation Requests:

If you need assistance with any part of the application or recruiting process due to a disability, or other physical or mental health conditions, please reach out to our Recruiting Accommodations Team through the Accommodation Request.

We are proud to be named as a Best Place to Work on Glassdoor in 2024 and be recognized for award-winning culture by organizations like Forbes, TIME, Disability:IN, and others.

Company:Expedia Group

Level of Experience:Senior (5+ years of experience)

Expedia is committed to creating an inclusive work environment with a diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, gender, sexual orientation, national origin, disability or age.

#J-18808-Ljbffr

Related Jobs

View all jobs

Senior Machine Learning Scientist

Senior Technical Lead, Machine Learning Science | Cardiff, UK

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer - Gen AI

Senior Machine Learning Engineer

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.