Machine Learning Engineer II - Travel LLMs Modeling

Design, build, and maintain scalable infrastructure for training and fine-tuning large language models on Booking.com's extensive data. Optimize model serving and inference pipelines for latency, throughput, and cost efficiency at production scale. Develop and maintain ML pipelines for data processing, model training, evaluation, and deployment. Implement model optimization techniques such as quantization, distillation, pruning, and efficient attention mechanisms to meet production requirements. Build monitoring, alerting, and observability systems for deployed ML models, ensuring reliability and performance in production. Collaborate closely with ML scientists to translate research prototypes into production-grade systems. Contribute to the development of reusable ML frameworks, tools, and libraries that accelerate the team's velocity. Ensure code quality, scalability, and maintainability through best engineering practices including testing, code reviews, and documentation.

We have found that people who match the following requirements are the ones who fit us best: Strong software engineering skills with deep experience in building and deploying ML systems at scale. Hands-on experience with LLM training infrastructure (distributed training, GPU clusters, frameworks like PyTorch, DeepSpeed, FSDP, or Megatron-LM). Experience with model serving and optimization (e.g., vLLM, TensorRT, ONNX, triton inference server, or similar). Relevant work or academic experience (BSc + 4 years of working experience, or MSc + 3 years of working experience) in software engineering with a focus on machine learning systems. Bachelor's, Master's degree or equivalent experience in Computer Science, Software Engineering, or a related quantitative field. Strong proficiency in Python; experience with Java, C++, or CUDA is a plus. Experience with cloud infrastructure and orchestration (Kubernetes, Docker, AWS/GCP) and distributed computing frameworks (Spark, Ray, or similar). Familiarity with ML experiment tracking, CI/CD for ML, and data versioning tools. Experience with large-scale data processing pipelines (Kafka, Hadoop, Spark, Airflow, or similar). Understanding of NLP/LLM concepts and the ability to collaborate effectively with ML scientists on model development. Excellent English communication skills, both written and verbal. Proven ability to work in a fast-paced, collaborative environment with cross-functional teams (ML scientists, product managers, developers).

Annual paid time off and generous paid leave scheme including: parental (22-weeks paid leave), grandparent, bereavement, and care leave Hybrid working including flexible working arrangements, working from home furniture and ergonomic support, and up to 20 days per year working from abroad (home country) A beautiful sustainable HQ Campus in Amsterdam, that offers on-site meals, coffee, and snacks, multi-faith and breastfeeding rooms at the office Commuting allowance and bike reimbursement scheme Discounts & Wallet credits to spend on our products, upgrade to Booking.com Genius Level 3, and friends & family Booking.com discount vouchers Free access to online learning platforms, development and mentorship programs Global Employee Assistance Program, free Headspace membership DEI: Diversity, Equity and

The team is at the forefront of Generative AI innovation, driving solutions for travel-related chatbots, text generation and summarization applications, Q&A systems, and free-text search. The team you are applying to is spearheading a research effort to develop leading LLMs, specifically designed for the travel domain and advanced agentic capabilities. This pioneering initiative combines cutting-edge AI research with practical applications, focusing on creating tailored solutions that redefine how travelers plan, book, and experience their journeys. Role Description As a Machine Learning Engineer, you will be responsible for building and maintaining the infrastructure, systems, and pipelines that enable the training, optimization, and deployment of cutting-edge Generative AI models at scale. This includes developing robust training pipelines for foundation models, optimizing model serving for low-latency inference, and ensuring production reliability for systems that serve millions of travelers daily. Your engineering expertise will be critical in bridging the gap between research breakthroughs and production-ready AI systems.