Machine Learning Engineer

ABOUT US 🚀

Hostelworld Group, the global hostel-focussed online booking platform, inspires adventurous minds to meet the world and come back with life-changing stories to tell. Our customers are not your average tourists, they crave cultural connection and unique experiences that we make possible by providing an unbeatable selection of hostels in unmissable locations – all in the palm of their hand.

It is the social nature and community feel of hostels and their environment, that enable travellers to embrace journeys of discovery, adventure and meaning. We have more than 13 million reviews across 17,800 hostels in more than 179 countries, making the brand the leading online hub for social travel. The website operates in 19 different languages and our mobile app in 13 languages.

Founded in 1999 and headquartered in Dublin, Hostelworld has a growing, high-calibre team of 230 people within Technology, Product, Global Markets, HR, Finance & Legal and Marketing Teams across our Dublin, London, Porto, Shanghai and Sydney offices. Hostelworld is listed on the London Stock Exchange and Dublin Euronext. You can read more about our story here.

LOCATION🌍

This role is based in Portugal. We have an office space in central Porto for those who prefer a hybrid model where you can spend time with colleagues in-person. If necessary, the role can be remote within Portugal with coming together for team meetings as needed.

WHO YOU'LL WORK WITH 👨🏽‍🤝‍👨🏼

Hostelworld is structured differently than most tech companies in world, as we do not have an Engineering or Product department. Instead, we have “Growth Teams” which are full stack groups that are organised into cross functional squads. As a Machine Learning Engineer, you will play a key role in shaping the Data Science Growth Team progress on building products that help travellers find the right inventory of hostels and experiences.

WHAT YOU'LL DO 👩‍💻

• Translate business and customers problems into machine learning solutions.
• Defining hypotheses, measure and monitor the success.
• Propose, design, build, maintain and push machine learning solutions into production.
• Express and communicate the business and technical impact of the machine learning solutions to a variety of audiences.
• Responsible for the end-to-end process of designing and running experiments to serving production models at scale.
• Ensure that the solutions are maintainable, performant, scalable and debuggable.
• Automate and abstract away different repeatable routines that are present in most machine learning tasks.
• Provide support to software engineers and product managers building machine learning solutions.
• Bring the best software development and infrastructure practices to the data science squad.

WHAT WE’RE LOOKING FOR 👀

Experience & Qualifications:

• Proficiency in machine learning concepts such as data exploration, feature engineering, model selection, training and testing.
• • Proven experience in productization of machine learning solutions – libraries, packages, REST APIs, serving and monitoring.
• • Familiarity with ranking, recommender systems, and natural language understanding.
• • Familiarity with deep learning.

Knowledge & Skills:

• Proficiency programming in Python and SQL-like databases.
• You are proficient in at least one of the following machine learning tasks – classification, regression or clustering.
• You are familiar with at least two of the following libraries or packages – scikit-learn, pandas, Spark MLlib, TensorFlow or PyTorch.
• Ability to carefully design solutions that scale with increasing volumes of data.
• Ability to follow software engineering best practices, including requirements analysis, system design, modularity, version control, testing and documentation

WHAT WE OFFER 💯

• Competitive salary & benefits
• Enhanced annual leave plus 3 Wellbeing Days per year
• Paid family leave (maternity, paternity, surrogacy & adoption)
• Agile working (plus a Working from Abroad Policy!)
• Support for your ongoing growth & development
• Inclusive people policies (sickness, menopause, compassionate and fertility leave)
• A chance to give back to your local community with 5 paid volunteering days

We believe in talented and diverse teams that reflect the diversity of our customers and the communities in which we operate. Everyone brings different perspectives and experiences. We lay out the above requirements to guide applicants to the experiences that we believe will allow you to be successful in the role. If you don’t meet them all, please consider applying if you think you can still perform the role as described.