Senior Machine Learning Engineer

at  Bookingcom

At Booking.com, we want to empower everyone to experience the world. Through our products, partners, and people are how we do it. There’s a whole planet of possibilities out there, and we bring it all together, in one place. Booking.com (USA), Inc, one of the support companies in the United States, is seeking a full time Senior Machine Learning Engineer role.

ROLE DESCRIPTION:

As a Senior Machine Learning Engineer you are a driver of your subject area, who generates significant business impact directly and indirectly, and crafts the direction of product initiatives by being driven and having full ownership of your projects - from conceptualization to collecting data, annotating, feature engineering, productionizing and training model pipelines, deploying experiments, measuring and iterating on them - as well as coaching other Machine Learning Engineers, Scientists, Data Engineers, Product Managers, etc to do the same.
At Booking.com, data drives our decisions. To do it more efficiently we are building a platform for data scientists. Our Machine Learning Platform is behind what makes Booking able to deliver personal experiences and you will be a part of its development helping to support multiple areas of the business as they use ML to address some of the hardest problems that we face, from detecting fraud to recommending which properties you should check for your next stay.
Our Gen AI Foundation team is responsible for developing the tools and infrastructure within the ML platform. Our primary objective is to solve business challenges by creating innovative and advanced Gen AI applications. These applications are designed to cater to the needs of both internal and external users.
This role is based out of Washington, DC, USA. You will have your product team in Washington, DC but the majority of the stakeholders and users will be in Amsterdam and Tel Aviv. This role would require in-frequent travel (~2 times a year) to Amsterdam / Tel Aviv and adapting your working hours to be able to communicate and collaborate with stakeholders.

WHAT YOU’LL BE DOING:

• Translate specific business problems into ML/AI challenges and identify the best approach within the constraints of the production environment. Build proof-of-concepts to test new insights and demonstrate their potential value to relevant partners.
• Develop production-grade machine learning code, from models to features and pipelines, allowing for scalability, realtime inference, monitoring and retraining. Monitor product health, performance and business impact and act accordingly when not met.
• Build readable and reusable code, choosing the right technologies, coding methodologies, and approach from carefully designed rapid prototyping to software deployment at scale. Find opportunities for platform-based development and reuse by abstracting business problems to generalized ML/AI solutions.
• Implement applied research plans for machine intelligence on a specific product by designing innovative ML/AI models, algorithms, and approaches that deliver both short-term commercial impact and longer-term differentiated business value and customer experiences. Document and share the findings.
• Maintain a highly cross-disciplinary perspective, solving issues by applying approaches and methods from across a variety of ML/AI subject areas and related fields. Mentor others through evidence and clear communication, explaining sophisticated technical concepts.
• Identify underlying issues and opportunities across domains and situations through application of structured thinking and logic, and formulate possible improvements.
• Continuously evolve your craft by keeping up to date with the latest developments in ML/AI and related technologies, introducing them to the machine learning community and promoting their application in areas where they can generate impact.
• Actively supply to Machine Learning at Booking.com through training, exploration of new technologies, interviewing, onboarding and mentoring colleagues.
• Push for improvements, scaling and extending machine learning tooling and infrastructure, collaborating with central teams.