Top Machine Learning Jobs in Amsterdam

Lead and grow a ~50-person ML organization, prioritize high-impact AI projects, set ML technical direction and standards, ensure delivery and measurable business value, and collaborate across products to translate AI research into product innovations.

Design and deploy intelligent systems, productionize models, collaborate with stakeholders, and develop tools for cloud-based Machine Learning platform.

Conduct applied AI/ML research to solve home design challenges, work with raw data, test hypotheses, and build prototypes/MVP solutions.

Lead and grow an ML infrastructure team building training frameworks, dataset pipelines, orchestration, experiment tracking, GPU/compute infrastructure, and release tooling to scale deep learning research into production. Drive performance, reliability, reproducibility, hire and mentor engineers, collaborate closely with researchers, and contribute hands‑on code to reduce training time and improve hardware utilization.

Nebius seeks a Senior ML Engineer for AI R&D focusing on applied research in reinforcement learning and agent training, requiring deep expertise in machine learning and software engineering.

Develop intelligent agents for a materials discovery engine, orchestrating scientific workflows and integrating experimental design for sustainability solutions.

Lead end-to-end ML work: define product problems as ML tasks, prototype and ship models (ranking, retrieval, LLM agents), build evaluation and monitoring, and measure marketplace impact (bookings, retention, GMV). Collaborate with engineering, product, and growth to deploy production models and improve LLM reliability.

Build and productionize large-scale TTS and adjacent speech models end-to-end: model architecture, training, alignment, evaluation, data curation, distributed GPU scaling, latency/cost profiling, tooling, and safety/mitigation for speech systems.

Senior technical lead embedding with customer teams to design, architect, and deliver production-grade AI/ML solutions. Drive model optimization, high-performance training (TPUs), efficient serving (vLLM), MLOps, agentic systems, GDPR-compliant private AI, ROI and cost optimization, pre-sales technical support, mentoring, and external thought leadership.

Design, prototype, and productionize ML models for pricing, carrier forecasting, and recommender systems. Lead applied research and product discovery, integrate LLMs and AI agents, collaborate with Product and MLOps to deploy and monitor models, and translate operational problems into measurable ML solutions that drive margin and efficiency.

Build and deploy production-ready ML models for pricing, carrier forecasting, and recommender systems. Conduct deep data science and statistical exploration, partner with product and operations to translate problems into ML solutions, integrate LLMs and AI agents, and collaborate with the MLOps platform to productionize and monitor end-to-end projects.

Lead the company’s ML/AI platform from experimentation through production, including SageMaker training infrastructure, model serving, inference pipelines, MLOps, and Java microservice integrations. Set AI/ML technical direction, develop GenAI and LLM retrieval and agent architectures, establish evaluation and safety guardrails, and partner with data science, DevOps, product, and engineering leadership on scalable ML roadmaps.

Design, deploy, and scale low-latency inference infrastructure for generative audio models (TTS, ASR, voice conversion). Build high-performance inference engines, Kubernetes-based autoscaling, CI/CD pipelines, observability, and GPU optimization to bridge research and production for streaming and batch workloads.

Design, prototype, and productionize ML models for pricing, carrier forecasting, and recommender systems. Conduct deep statistical analysis, partner with product and operations for discovery, integrate LLMs where appropriate, and collaborate with MLOps to deploy and monitor end-to-end solutions.

Seeking an AI/ML Engineer to develop and optimize machine learning models for mobile apps, collaborating with teams to enhance user experiences.

Design, develop, and deploy end-to-end machine learning solutions. Build scalable data pipelines and analytics using Python and SQL, develop statistical and predictive models, work with cloud ML platforms (Azure, AWS, GCP), optimize model performance, and collaborate with cross-functional teams to productionize and maintain ML systems.

Develop and ship features for Junie across CLI and model integrations, experiment with model setups and ensembling for cost-quality tradeoffs, build and test autonomous agent behaviors for long-term maintenance, and own projects from research to production while using Junie daily to iterate on quality and UX.

Design, build, and operate ML platform systems to train, evaluate, deploy, and serve generative models. Improve reliability, scalability, observability, and cost-efficiency of GPU and cloud workloads. Build internal tools and agent-friendly workflows, collaborate with researchers and engineers, and drive platform architecture and automation.

Own, design, build, and operate distributed, high-load backend services and data pipelines for ML infrastructure. Partner with ML and product teams to deliver self-service, reliable model serving, storage, and deployment components; ensure observability, scalability, and performance while driving technical decisions and continuous improvement.

Build and ship state-of-the-art speech systems end-to-end: model architecture, training (pre-train/fine-tune/post-train), evaluation, tooling, data curation, production inference, and safety/abuse mitigation for voice conversion and related tasks.

Design, train, and improve computer-vision and generative ML models; handle end-to-end ML pipeline from data collection to deployment; prototype research ideas and convert successful prototypes into product-ready solutions; optimize models for efficient on-device inference and collaborate with product teams to integrate models.