As a Machine Learning Engineer at Headout, you will play a pivotal role in developing AI-powered solutions that enhance our platform and create exceptional experiences for travelers worldwide. At Headout, we firmly believe that intelligent algorithms can transform how people discover and engage with travel experiences. Collaborating closely with multifaceted teams across the organization, you’ll design, develop, and deploy sophisticated ML models across various applications including recommendations, search optimization, pricing, and operational efficiency. 🌟 What makes the role stand out?

Global Impact: Your algorithms will serve millions of travelers across 190+ countries, optimizing experiences throughout the customer journey - from discovery and decision-making to post-purchase engagement. Diverse AI Applications: Work on a variety of machine learning projects spanning different domains. One day you might be improving our recommendation engine, the next you could be optimizing search rankings or developing forecasting models for operational planning. End-to-End Ownership: Take ML solutions from ideation to production. You’ll help identify opportunities where ML can add value, design solutions, implement models, and measure real-world impact. Data-Rich Environment: Leverage rich, multi-dimensional data from user behavior, transaction patterns, content characteristics, and operational metrics to build comprehensive models that drive meaningful outcomes. Tangible Results: See the concrete impact of your work through key business metrics. Your models will contribute to increased conversion rates, enhanced user engagement, optimized operations, and improved customer satisfaction. Technical Innovation: As machine learning and AI technologies evolve, you’ll be at the forefront of evaluating and implementing new approaches that keep Headout competitive in a dynamic industry.

🎯 What skills you need to have

You have a minimum of 4 years of experience in machine learning engineering across different applications such as recommendations, classification, prediction, or natural language processing. A strong foundation in ML fundamentals and techniques is essential. Proficiency in Python is a must, with experience in frameworks like TensorFlow, PyTorch, scikit-learn, or similar ML libraries (Hugging Face, XGBoo st etc). You have practical experience taking ML models from development to production, including data preprocessing, feature engineering, model training, evaluation, and deployment. Experience with A/B testing and experimentation frameworks to measure and validate the impact of ML solutions. You possess strong problem-solving skills and the ability to translate business requirements into effective technical implementations. Your communication skills enable you to work effectively with cross‑functional teams and explain complex technical concepts to non‑technical stakeholders. Familiarity with large‑scale data processing technologies (Spark, Kafka, Flink etc.) and cloud‑based ML services (Vertex AI, Sagemaker etc) is a plus

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