Top Machine Learning Jobs in Amsterdam

The Senior Machine Learning Engineer will design and implement ML pipelines, optimize LLM performance, and collaborate on AI infrastructure for cost-efficient solutions.

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.

The AI/ML Engineer role emphasizes technical leadership, project management, and client consultation, focusing on cloud solutions and system architecture. Candidates should have experience with data centers, systems architecture, and cloud technologies, aiming for innovative solutions within budget and time constraints.

Lead a team of researchers and engineers in developing and deploying advanced AI-driven recommender systems for eBay's eCommerce platform, leveraging ML and NLP techniques.

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.

Design, build, and maintain production-grade ML systems for revenue management. Own end-to-end ML lifecycle: data pipelines, model development, deployment, monitoring, and testing. Collaborate with product and engineering, define SLIs/SLOs, mentor engineers, and drive technical strategy to deliver scalable, reliable pricing and revenue-optimization features.

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

Senior ML Data Scientist responsible for building production-ready, scalable ML models, finding data-driven opportunities, deploying models (real-time, async, batch) on Kubernetes/cloud, collaborating with product and engineering, and mentoring others to deliver robust data products that create business value.

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.

Research and engineer large multimodal and reinforcement-learning models for robotic agents: design, train, evaluate models; prototype in simulation and on hardware; build data pipelines, simulation environments, and distributed training infrastructure; collaborate to scale promising approaches into production and publish results.

Design and build research prototypes and production-capable systems to enable portable, interoperable agent and model workflows. Develop model routing, provider abstractions, memory and context representations, benchmarking suites, and evaluation protocols. Run ML experiments, implement robust APIs and integration layers, collaborate across research and engineering, and communicate results via reports, demos, publications, and open-source releases.

Build and deploy production ML systems for large-scale ecommerce search and recommendations using text, image, and multimodal approaches. Tune and integrate LLMs and transformer models, develop end-to-end visual solutions, improve business KPIs through experimentation, and collaborate across teams to productize and scale ML capabilities.

Advise AI-focused customers to design scalable AI solutions and manage large-scale GPU deployments. Deliver technical presentations, documentation, and webinars. Collaborate with engineering and product teams to prioritize and convey customer feedback.

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.

Optimize ML model training pipelines for speed and reliability, applying GPU optimization techniques and mentoring team members on performance best practices.

Design and build end-to-end ML systems for fraud detection: develop and deploy models, create production-ready features and real-time data pipelines, integrate with backend services, and ensure monitoring, security, and observability.

Design, train, fine-tune, distill, and deploy LLM-based and agentic ML models. Build and operate agent workflows in Python, run evaluation and benchmarking, select models and cost strategies, and manage production monitoring and observability for model performance and reliability.

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.