Machine Learning Engineer, FareHarbor

• 3+ years of experience working on production ML systems
• Strong Python and SQL skills
• Solid foundation in applied statistics and machine learning theory
• Hands-on experience with end-to-end ML pipelines development and lifecycle support (data pipeline, feature generation, model training, deployment and inference, monitoring, rollback strategies)
• Understanding of data pipeline orchestration principles (Airflow, dbt)
• Experience with Cloud Computing Platforms (preferred AWS: S3, EC2, ECR, SQS)
• Experience designing and evaluating experiments (A/B tests) and tying results to business metrics
• Experience with common ML frameworks (scikit-learn, gradient boosting frameworks, PyTorch or TensorFlow)

Desirable

• FareHarbor (a Booking Holdings company) is looking for a Machine Learning Engineer to join our Pricing & Revenue Management domain
• You’ll work on ML systems that optimize pricing, revenue, and marketplace efficiency, owning models from experimentation through production
• This role is focused on hands-on model development and production delivery, with growing ownership over systems and experiments
• Design and build scalable, reliable backend features across our products
• Translate business requirements into practical technical solutions
• Estimate effort, deliver incrementally, and communicate progress clearly
• Prioritize tasks, identify dependencies, and flag risks early
• Troubleshoot production issues and improve reliability through root-cause analysis
• Contribute to secure coding practices and review code with a security mindset
• Collaborate closely with product, design, and other engineering teams
• Help improve team processes and share knowledge with teammates
• Design, implement, and evaluate ML models for revenue and margin optimization
• Translate pricing and revenue requirements into concrete optimization or prediction problems
• Apply supervised learning, reinforced learning (eg mult armed or contextual bandits) and statistical techniques to structured, real-world pricing data
• Design and run A/B tests and controlled experiments to validate pricing strategies
• Define, track, and interpret key metrics such as revenue, conversion, utilization, and margin
• Perform post-experiment analysis and translate results into model improvements and product changes
• Develop and maintain production-grade ML pipelines and services for pricing use cases
• Integrate monitoring to ensure robustness, reliability, and safe rollouts
• Collaborate with backend engineers to integrate ML pricing services into the FareHarbor platform