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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer II - Travel LLMs Modeling - **Company:** Booking.com - **Location:** Amsterdam, Netherlands (Remote available) - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Airflow, Amazon Web Services, C++ (Programming Language), Cloud Computing, Computer Clusters, Software Quality, Code Review, Nvidia CUDA, Continuous Integration, Distributed Computing Environment, Apache Hadoop, Python (Programming Language), Machine Learning, Tensorflow, Software Engineering, Data Processing, Pytorch, Large Language Models, Apache Spark, Generative AI, Kubernetes, Information Technology, Low Latency, ONNX (Open Neural Network Exchange) Format, Apache Kafka, Machine Learning Operations, TensorRT, Docker - **Published:** August 8, 2026 - **Apply:** https://nl.indeed.com/viewjob?jk=98a25fe365b9c6bc ## About the Role 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). ## 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., * 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. ## Related Videos - [LLMs in the wild: Building an AI agent that survives production](https://www.wearedevelopers.com/videos/100319-llms-in-the-wild-building-an-ai-agent-that-survives-production) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies in Europe](https://www.wearedevelopers.com/magazine/162-highest-paying-tech-companies-in-europe) - [Fully Remote Software Engineer Jobs](https://www.wearedevelopers.com/magazine/447-fully-remote-software-engineer-jobs) - [7 Most Popular Web Developer Jobs in Europe](https://www.wearedevelopers.com/magazine/163-7-most-popular-web-developer-jobs-in-europe)