Top Machine Learning Jobs in Amsterdam

Own the strategy, vision, priorities, and roadmap for Adyen’s data developer experience platform. Partner with platform and engineering teams to consolidate tools, improve developer autonomy and productivity, drive adoption, and support data and AI product delivery. Engage data engineers, machine learning scientists, AI engineers, analysts, product managers, and operational stakeholders to identify needs, make strategic trade-offs, and deliver scalable platform capabilities.

Lead the strategy and execution of Adyen’s AML detection product. Build scalable machine learning models, rules, and risk signals; develop self-service detection using ML, GenAI, and agentic approaches; establish effectiveness metrics; and partner with Compliance, Legal, Risk, Operations, data scientists, analysts, and engineers. The role requires prioritization, regulatory judgment, stakeholder influence, and leadership of a cross-functional product team in a highly regulated fintech environment.

Build and operate production machine learning pipelines for Adyen’s anti-money laundering systems. Modernize rules-based detection into an AI/ML engine, develop models using large-scale transaction data, address class imbalance and incomplete labels, and collaborate with data engineering, analytics, operations, and compliance teams. Lead projects from ideation through deployment while promoting strong software engineering, data engineering, and MLOps practices.

As a Senior AI Research Engineer, you will drive Adyen's AI strategy, build production-grade applications, optimize AI interactions in finance, mentor teams, and engage with the AI community.

Leads regional digital technology service delivery across EMENA and GBI, overseeing infrastructure operations, budgets, vendors, manufacturing-plant integrations, service performance, and technology strategy. The role drives AI-first and cloud-first adoption, operational improvements, responsible AI practices, organizational change, and stakeholder engagement across multiple countries. It also manages regional teams, supports new plant launches, ensures Industry 4.0 alignment, and delivers cost, compliance, and customer satisfaction targets.

As a Data Analyst, you'll advise clients on data management strategies, implement solutions, and enhance data governance and quality. You'll also provide insights for business decisions based on analytical data evaluations.

Leads enterprise-wide Finance transformation, advising the CFO and Finance Leadership Team while modernizing operating models, processes, technology, data, and governance. Oversees organizational redesign, shared services, location strategy, automation, AI adoption, finance systems, continuous improvement, program governance, change management, and executive stakeholder alignment. Builds and leads a transformation office, establishes roadmaps and success metrics, manages risks and benefits, and develops high-performing teams.

Supports clients across industries with AI and data strategy, operating model design, investment and value cases, and AI product roadmaps. The role involves analyzing business opportunities, advising on artificial intelligence and machine learning applications, presenting insights, building client relationships, and contributing to innovation and internal initiatives. Candidates should have a relevant master’s degree, strong analytical and communication skills, and fluency in Dutch and English.

Works with clients to solve business problems using data science, statistical modeling, machine learning, deep learning, generative AI, agentic AI, and NLP. Responsibilities include data cleansing, preprocessing, analysis, visualization, proof-of-concept development, stakeholder workshops, and deploying production-ready models on cloud platforms. The role translates technical insights into business recommendations and supports strategic outcomes.

Own end-to-end data science projects from scoping through production deployment. Design and evaluate models, build ML and AI workflows involving entity resolution, record linkage, embeddings, semantic similarity, and LLMs, and apply DataOps and MLOps practices. Develop reusable tools, collaborate with product and engineering, create technical documentation, review code, and mentor junior data scientists.

Leads CAE’s enterprise Safety and Quality Intelligence capability by standardizing analytics, data governance, KPIs, taxonomies, dashboards, and reporting. Partners with governance, operations, manufacturing, and business-unit leaders to develop an intelligence platform incorporating predictive analytics, automation, and AI. Establishes executive risk and performance insights, influences global stakeholders, and leads a team of analytics professionals.

Analyze large-scale gaming datasets using statistics, machine learning, forecasting, recommender systems, generative AI, experimentation, causal inference, and econometrics. Partner with product and engineering teams to develop data-driven strategies, build business solutions, deliver actionable insights, and share technical knowledge across teams.

Lead scientific delivery for partner materials-discovery projects, from scoping through results. Apply simulation, machine learning, data-driven optimization, or experiments; design discovery campaigns; validate models against experiments; and translate between experimental, computational, and ML teams. Build reliable automated workflows, improve data pipelines, prepare technical reports, and relay partner feedback to technical teams. The role requires deep expertise in materials science, chemistry, physics, chemical engineering, or related fields, along with Python and Git proficiency.

Own and improve production machine learning pipelines focused on accuracy, efficiency, and trustworthiness. Build LLM evaluations, judges, datasets, metrics, and benchmarks; run experiments across prompts, models, and pipeline designs; and reduce LLM costs through smaller task-specific models. Expand anonymization capabilities, evaluate open-source models, and collaborate with quality engineers to ship reliable ML features. Document findings and communicate technical trade-offs clearly.

Design, implement, deploy, and monitor production machine learning systems for payment optimization, identity verification, fraud prevention, AML, and behavioral analysis. Build large-scale data and training pipelines, automate experimentation and model monitoring, analyze complex datasets, conduct statistical experiments, and define performance metrics. Collaborate with product managers and stakeholders while providing hands-on technical leadership across the machine learning lifecycle.

Build and operate production machine learning systems at scale, including data ingestion, training, validation, deployment, and monitoring pipelines. Optimize model training and inference performance, integrate ML solutions with products, and transition research prototypes into scalable services. Collaborate with software engineering, data science, MLOps, and platform teams across payment optimization, identity verification, disputes, AML, fraud prevention, and anomaly detection use cases.

Develops data analytics, data science, and AI solutions for internal audit, including pipelines, automations, APIs, machine-learning analyses, and reusable tools. Translates audit and risk questions into reliable, evidence-based insights; strengthens data governance and code quality; and trains auditors through coaching and bootcamps. The role also drives the internal audit data and AI roadmap, supports technology innovation, and collaborates internationally across Ayvens and Societe Generale.

Lead the Production Inference team of research scientists and ML engineers. Own people development, technical direction, research and development roadmaps, architecture decisions, and model-serving reliability, efficiency, latency, and cost performance. Partner with research, infrastructure, product, and commercial stakeholders; guide production deployment and lifecycle improvements; and recruit technical talent. The role emphasizes leadership and technical reviews rather than hands-on coding.

Conduct quantitative research on market behavior and trading strategies. Responsibilities include discovering predictive patterns, designing statistical and machine-learning models, building AI research agents, developing large-scale research infrastructure, running experiments and backtests, and translating findings into production-ready trading signals. Interns complete an end-to-end research project while collaborating with researchers and technologists.

Conduct quantitative research on financial markets by discovering predictive patterns, developing statistical and machine learning models, running large-scale simulations and backtests, and translating research into production-ready trading signals. Interns collaborate with researchers and technologists, build research infrastructure and AI-powered agents, and complete rigorous projects from hypothesis through evaluation under realistic market conditions.

Conduct quantitative research for systematic trading by analyzing market data, formulating and testing hypotheses, developing statistical and machine learning models, backtesting strategies, studying market microstructure, and partnering with technologists to deploy production-ready research and trading systems.

Lead and develop machine learning, LLM, and automation projects using large-scale network and device telemetry. Clean and transform data, engineer features, build models for anomaly detection, forecasting, and optimization, and deliver reproducible analyses with Python and SQL. Collaborate with engineering and product teams to deploy, integrate, and monitor models in production while contributing to MLOps workflows.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Develop and deploy machine learning models for retrieval, reranking, indexing, search relevance, crawling, and content understanding. Build large-scale embedding and retrieval systems, evaluation pipelines, and quality metrics for agent-native search. Integrate models into high-throughput production services while balancing latency, quality, and cost. Collaborate on architecture and product decisions involving search, retrieval, and LLM-based systems.