Booking.com is hiring a Machine Learning Engineering Manager

Booking.com is looking for a Machine Learning Engineering Manager to lead a team focused on the foundational ML and data layers that power our ranking and recommendation systems. You will drive the development of robust, scalable data and ML pipelines and implement tools to help ML scientists test and productionize advanced solutions.

What You'll Do

• Lead and develop a high-performing team of Machine Learning Engineers and Data Engineers, fostering individual growth and collaboration.
• Manage and mentor engineers, ensuring their professional development and effectiveness.
• Develop scalable ML infrastructure and pipelines for efficient data processing and model deployment.
• Evaluate architecture solutions based on cost, business needs, and emerging technologies.
• Collaborate closely with software engineers to ensure seamless model deployment and inference.
• Monitor application health, set and track relevant metrics, and implement effective maintenance strategies.
• Collaborate with stakeholders to translate business requirements into viable ML solutions.
• Evaluate and integrate new ML technologies to enhance productivity and performance.
• Drive continuous improvement through model retraining, performance monitoring, and optimization.
• Develop robust ML and AI solutions that meet business objectives while considering production constraints.
• Stay abreast of industry methodologies, explore new technologies, and champion their adoption.
• Actively contribute to Machine Learning at Booking.com through training, exploration of new technologies, and mentoring colleagues.
• Advocate for improvements, scaling, and extension of ML tooling and infrastructure.
• Foster a culture of innovation, collaboration, and excellence within the ML team.

What We're Looking For

• 3+ years leading an ML engineering team of a minimum of 4 people in a fast-paced production environment.
• Relevant work or academic experience (MSc + 5 years of working experience, or PhD + 3 years of working experience), involved in the application of Machine Learning to business problems.
• Masters degree, PhD or equivalent experience in a quantitative field (e.g. Computer Science, Engineering, Mathematics, Artificial Intelligence, Physics, etc.).
• Strong knowledge in areas like Recommender Systems, Deep Learning, Information Retrieval, Causal Inference, and scaling ML models.
• Experience designing and executing end-to-end solutions for deploying different ML models.
• Experience with cloud frameworks like AWS SageMaker for training, evaluation and serving models using TensorFlow, PyTorch, or scikit-learn.
• Experience with big data processing frameworks such as Pyspark, Apache Flink, Snowflake or similar.
• Demonstrable experience with MySQL, Cassandra, DynamoDB or similar relational/NoSQL database systems.
• Deep understanding of machine learning algorithms, statistical models, and data structures.
• Experience collaborating cross-functionally in the development of machine learning products (e.g. with Developers, UX specialists, Product Managers).
• Strong working knowledge of Python, Java, Kafka, Hadoop, SQL, and Spark or similar technologies. Working experience with version control systems.
• Excellent English communication skills, both written and verbal.
• Successfully driving technical, business and people related initiatives that improve productivity, performance and quality while communicating with stakeholders at all levels.
• Leading by example, gaining respect through actions, not your title. Developing your team and motivating them to achieve their goals. Providing feedback timely and managing your key team performance indicators.

Technical Stack

• Cloud & ML: AWS SageMaker, TensorFlow, PyTorch, scikit-learn
• Data Processing: Pyspark, Apache Flink, Snowflake
• Databases: MySQL, Cassandra, DynamoDB
• Languages & Tools: Python, Java, Kafka, Hadoop, SQL, Spark

Team & Environment

You will lead a team of Machine Learning Engineers and Data Engineers that is part of the Ranking & Recommendations track.

Benefits & Compensation

• Annual paid time off and generous paid leave scheme including: parent, grandparent, bereavement, and care leave.
• Hybrid working including flexible working arrangements, and up to 29 days per year working from abroad (home country).
• Industry leading product discounts - up to 1400 per year - for yourself, including automatic Genius Level 3 status and Booking.com wallet credit.

Work Mode

This position offers a hybrid work model.

Booking.com is proud to be an equal opportunity workplace and is an affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.