Top Machine Learning Jobs in Amsterdam

Join as a graduate quantitative researcher to design, build, test and deploy statistical and machine learning models for pricing, forecasting and signal discovery using large-scale market and unstructured data. Collaborate with researchers, traders and engineers to monitor production models, refine assumptions, and accelerate research-to-production using AI-enabled workflows.

Build and productionize multi-agent systems and agentic workflows for personalized e-commerce recommendations. Design agent coordination, communication, planning, evaluation, tool use, safety controls, and orchestration techniques. Address latency, cost, and reliability challenges at eBay scale while collaborating with researchers, engineers, product, design, and analytics teams. Use large multimodal datasets and A/B testing to improve marketplace outcomes.

Architect and build the AI layer for Neo4j’s Hume Maestro platform. Responsibilities include setting the AI technical roadmap, contributing hands-on to production code, designing scalable AI architectures, establishing model evaluation and deployment practices, integrating AI with front-end and platform systems, and collaborating with Product and Science stakeholders. The role may eventually evolve into leading and mentoring a dedicated AI engineering team.

The AI Lead is responsible for managing AI teams, driving AI project strategies, and ensuring deployment efficiency in telecommunications. They will oversee AI R&D, mentor team members, and collaborate across teams to align AI solutions with business goals.

Conduct original research in AI and machine learning, develop deep learning architectures, analyze large datasets, and explore new methodologies.

Lead product strategy and execution to grow buyer discovery, engagement, conversion and retention. Prioritise opportunities using data and research, run experiments and A/B tests, partner with engineering, design and data science, and scale AI-enabled recommendation and personalization features to deliver measurable customer and business outcomes.

The Data Scientist will design algorithms for insights, structure data, collaborate with machine learning engineers, and analyze datasets to improve workflow automation and product value.

The Applied Scientist will solve impactful problems using deep learning models and statistical analysis, translating commercial goals into technical solutions.

As an AI Product Manager, you will define the product roadmap, collaborate with teams to develop AI features, conduct market research, and ensure ethical AI use.

Own and evolve a cloud-agnostic data and ML platform supporting ingestion, storage, processing, model training, deployment, and lifecycle management. Build reliable, scalable, automated infrastructure using DevOps and MLOps practices, including CI/CD, Infrastructure-as-Code, containerization, monitoring, governance, security, and cost optimization. Collaborate with data science, ML engineering, and IT teams while maintaining vendor-delivered components, documentation, and operational processes.

Work on client-facing analytics projects: design solutions, build prototypes, implement ML/NLP models (e.g., PD models, decarbonization, churn, GenAI chatbots), validate results, and communicate findings to clients and colleagues across industries.

Build, operate, and improve production services that deliver machine learning models within exocad’s AI Design product. Responsibilities include model serving APIs, containerized deployments, versioning, staged rollouts, rollback automation, infrastructure provisioning with Terraform and AWS, monitoring, observability, and performance tuning. The role also contributes to training and labeling infrastructure and requires collaboration across engineering, product, and global teams, including work across AWS global and China environments.

Design, build, and deploy scalable AI/ML and Generative AI solutions for enterprise systems. Develop Python-based production applications, APIs, microservices, and MLOps/CI-CD pipelines. Implement data ingestion, preprocessing, feature engineering, model training/validation, deployment, monitoring, and lifecycle management. Collaborate with DevOps, data engineering, and product teams to ensure secure, compliant, and high-performance AI solutions.

Define and execute the product roadmap for AI-driven blockchain security monitoring tools. Collaborate with cross-functional teams, analyze market and customer feedback, deliver features on time, and communicate with stakeholders to align expectations and improve product offerings.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

As an AI Research Engineer, you will develop and optimize vision-language models, managing the full model lifecycle from training to deployment, focusing on efficiency and innovation in multimodal architectures.

Own Go-To-Market for performance media buyers: build relationships, close and onboard clients, represent Kayzen at industry events, advise prospects on product strategy, and funnel market feedback across teams.

As a Machine Learning Research Intern, you will conduct research on HFT strategies, analyze market opportunities, and collaborate on strategy implementation using ML techniques.

Join a trading team to analyze global markets and develop profitable trading strategies using machine learning and statistical models.

The Senior Quantitative Researcher will lead the development and deployment of ML-based statistical arbitrage strategies while mentoring team members and collaborating with internal teams.

Build and maintain scalable backend systems that integrate real-time data, machine learning, and optimization models to optimize renewable and battery storage operations. Own architecture through deployment and maintenance, collaborate with engineers and data scientists, troubleshoot performance issues, and improve monitoring, reliability, and system performance.

Lead design and implementation of ML/LLM systems for ingestion, knowledge extraction, retrieval, and reasoning. Build datasets, metrics, pipelines, agentic retrieval and RAG solutions. Establish MLOps, orchestration, observability, and experiment tracking. Collaborate on research agenda, shape ML architecture, and hire and grow the ML team.

Lead benchmarking and profiling of GPU platforms for ML/AI workloads. Profile system and kernel-level GPU performance, debug and optimize ML training and inference, perform acceptance testing of GPU clusters, run experiments across GPU configurations and interconnects, and develop tooling and dashboards to visualize performance and bottlenecks.

The role involves designing and developing model evaluation pipelines, user studies for subjective evaluations, and automated dashboards to report results, alongside leading the evaluation team and communicating with other teams to improve model performance.