Top Machine Learning Jobs in the Netherlands

Develop and evaluate deep learning models for therapeutic antibody discovery and engineering. Compare models, design experiments, assess statistical validity, and deploy reliable model services with monitoring and versioning. Build AI platform features, including agent-based analysis tools, while collaborating with bioinformaticians and software engineers. Explain results to non-specialists, track advances in protein and antibody machine learning, and mentor junior colleagues and interns.

Conduct research across the full machine learning lifecycle, including ideation, model design, implementation, evaluation, training, inference optimization, and deployment. Develop novel machine learning methods and foundation models, establish evaluation protocols, scale training with GPUs or TPUs, and improve research engineering infrastructure for rapid experimentation.

Develop and operate production machine learning models and end-to-end MLOps pipelines for airline ancillary pricing. Responsibilities include model training, retraining, deployment, monitoring, architecture optimization, low-latency serving, infrastructure automation with Terraform, containerization with Docker, and CI/CD using GitHub Actions. The role uses GCP, BigQuery, and Vertex AI while leading MLOps practices and collaborating with data science and engineering teams.

Lead architecture and technical strategy for merchant fraud prevention across multiple teams. Design high-throughput, low-latency distributed systems, integrate ML/LLM models, evolve data and inference pipelines, align product and compliance goals, standardize practices, and mentor senior engineers.

Own the full enterprise sales cycle across the Benelux region, from territory development and partner engagement through solution demonstrations and deal closure. Present Darktrace’s cybersecurity solutions to CISOs and information security leaders, attend industry events, and collaborate with SDRs and technical experts. The role requires Dutch and English fluency, enterprise B2B closing experience, strong communication skills, entrepreneurial territory-building ability, and interest in cybersecurity within a high-growth environment.

Designs, tests, and maintains high-fidelity security detections across SIEM and related platforms. Conducts regression testing, breach and attack simulations, threat intelligence integration, MITRE ATT&CK mapping, detection tuning, and purple team exercises. Automates enrichment and triage through SOAR playbooks and scripts while partnering with incident response and threat hunting teams. Maintains detection documentation and measures coverage, false positives, and validation outcomes across cloud, endpoint, containerized, and modern application environments.

Provides strategic and hands-on biostatistical leadership across pharmaceutical discovery, translational, biomarker, and early research programs. Designs studies, analyzes complex biological datasets, develops advanced statistical methods and reproducible R workflows, and translates evidence into portfolio decisions. The role collaborates with quantitative and scientific functions, supports publications and external partnerships, mentors statisticians, and advances data-driven analytical practices.

Leads the training and deployment of domain-specific language models at scale. Responsibilities include designing experiments, improving model quality and efficiency, building distributed multi-node GPU infrastructure, preparing 100B+ token datasets, generating synthetic data, optimizing inference, creating evaluation frameworks, debugging training stability, and mentoring ML engineers and interns. The role requires production-grade Python and PyTorch expertise, technical leadership, and hands-on experience with large-scale language-model training and deployment.

Train, fine-tune, evaluate, and serve open-source large language models such as Qwen and Mistral. Design experiments, implement research findings, and contribute across the machine learning lifecycle, including production serving infrastructure. The role involves Python, PyTorch, Hugging Face, model training, data processing, distributed systems, and collaboration with AI engineers. This is a 6–12 month internship based in Amsterdam with at least three office days weekly.

Own the full sales cycle for technically complex B2B deals involving APIs, on-premises deployments, and custom licensing. Manage multi-stakeholder negotiations, build strategic customer relationships, drive retention and expansion, and develop sales processes in an early-stage environment. Collaborate with engineering, research, product, solutions engineering, and legal teams while translating customer needs into product feedback.

Leads go-to-market strategy, enterprise sales, account growth, and revenue targets for AI-powered cybersecurity solutions. Engages CISOs and senior security executives, translates technical capabilities into business and risk outcomes, manages complex sales cycles and seven-figure deals, develops offerings with technical and delivery teams, maintains forecasts and CRM pipelines, and represents Booz Allen at industry forums.

Supports Oliver Wyman’s Data and Analytics consulting team by designing analytical solutions, developing prototypes, applying machine learning and AI techniques, validating models, and assessing their effectiveness. The role involves collaborating with consultants and clients, explaining complex data science concepts clearly, and contributing to projects across financial services, sustainability, retail, transportation, and other industries. International travel and continuous technology development are expected.

Develop analytical solutions for consulting clients across financial services, sustainability, energy, retail, and transportation. Responsibilities include designing and prototyping data science solutions, applying machine learning and advanced analytics, validating models and GenAI applications, and communicating complex findings to clients and colleagues. The role involves collaboration across global teams, continuous learning, and potential international travel.

Collaborate with consultants and clients to design, prototype, validate, and implement data and analytics solutions. Projects may involve NLP, machine learning, GenAI validation, credit risk modeling, decarbonization, promotion optimization, supply chain risk, and customer churn. Explain sophisticated analytical concepts clearly, contribute to client discussions, and develop quantitative problem-solving skills in a collaborative consulting environment.

Develop analytical and machine learning solutions for global consulting clients, including prototypes, predictive models, NLP, GenAI validation, risk analytics, and optimization tools. Collaborate with clients and colleagues to explain sophisticated data science concepts, assess solution effectiveness, and deliver impactful results across industries such as financial services, energy, retail, and transportation.

Own production optimization of LLM and VLM inference systems, improving latency, throughput, memory efficiency, GPU utilization, quality, reliability, and cost. Deploy and benchmark inference engines, build compression and quantization workflows, implement acceleration techniques, diagnose serving bottlenecks, and collaborate with kernel, platform, research, product, and customer teams.

Lead product vision and roadmap for agentic AI solutions, own the backlog, translate business needs into user stories, partner with data scientists and engineers, manage Agile delivery cycles, ensure responsible AI practices, and monitor model performance and continuous improvement.

Build end-to-end machine learning systems spanning research reproduction, model evaluation, inference infrastructure, backend services, and product interfaces. Design rigorous experiments, evaluation datasets, and verification workflows; analyze model internals; investigate provenance and evasion; and convert findings into reliable production systems with APIs, background jobs, observability, testing, documentation, and user-facing React/TypeScript experiences.

Lead design, development, and production of machine learning models and systems for search relevance and retrieval. Improve ranking, embeddings, and ranking features, run offline evaluation and online experiments (A/B tests), optimize latency and scalability, collaborate with engineers and product teams, and mentor junior engineers.

Lead end-to-end architecture, implementation, and delivery of Blue Yonder Warehouse Management and related execution solutions. Serve as the primary client contact, manage timelines, budgets, risks, and deliverables, and lead solution teams and integrations. Provide functional and technical guidance, mentor junior staff, align solutions with business objectives, and collaborate with project managers, delivery partners, and stakeholders to deliver successful client outcomes.

Leads and grows a distributed EU-based AI and Machine Learning team, managing Data Scientists and Machine Learning Engineers. Responsibilities include line management, coaching, performance reviews, team culture, delivery processes, engineering quality, CI/CD, cloud automation, monitoring, architecture discussions, process improvement, and cross-regional stakeholder collaboration. The role partners with AI leadership on technical, organizational, and HR matters.

Lead developer relations for NVIDIA technologies across the EMEA energy and utilities ecosystem. Build strategic relationships with power, utility, grid, simulation, digital twin, and renewable energy software providers. Guide partner onboarding, integration, technical enablement, and co-innovation while identifying growth opportunities and shaping adoption strategies. Collaborate with engineering, product, marketing, and solution architecture teams; communicate technical concepts to diverse audiences; and advocate for partner feedback to influence product roadmaps.

Drive enterprise sales growth across the Benelux market by acquiring new customers, expanding existing accounts, and achieving revenue targets. Develop strategic account plans, lead complex customer engagements, present tailored AI and software solutions, and manage long-term customer relationships. Collaborate with solution consultants, customer success, professional services, marketing, partnerships, and product teams to support renewals, implementation, retention, and product feedback.

Lead the strategy, design, deployment, and scale-up of AI-powered health products across the Global South. Own product roadmaps, execution, government-system integration, stakeholder adoption, partnerships, grant development, and portfolio expansion. Collaborate with engineering, machine learning, program, and domain teams to deliver sustainable, measurable public-health impact.

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.