senior machine learning engineer Jobs in Amsterdam

senior machine learning engineer

ai engineer

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data & ml infrastructure engineer Jobs in Amsterdam

data & ml infrastructure engineer

it platform engineer innovation Jobs in Amsterdam

it platform engineer innovation

ml platform engineer

kaiko.ai

<div>
 
 About Kaiko.ai
 
 
 
 Kaiko is building a next-generation agentic clinical AI assistant that helps clinicians reason across patient data, guidelines, and diagnostics.
 
 
 Healthcare decisions are rarely made by a single person or from a single data source. kaiko's assistant maintains longitudinal patient context across encounters, clinicians, and institutions, enabling collaboration, second opinions, and complex diagnostic workflows. The system is designed to operate safely in real clinical environments, with human oversight, auditability, and regulatory alignment at its core.
 
 
 Our assistant core supports broadly applicable clinical tasks such as patient data navigation, guideline interaction, multimodal interaction (chat and voice), and care coordination. On top of this foundation, we are developing specialized diagnostic agents in areas such as oncology, radiology, and pathology.
 
 
 We build in close collaboration with leading hospitals and research centers, including the Netherlands Cancer Institute (NKI). kaiko is a well-funded company with a growing international team, operating from Zurich and Amsterdam.
 
 
 
 About The Role
 
 
 
 As an ML Platform Engineer, you'll help architect, scale, and evolve the infrastructure that powers kaiko's foundation-model training and serving across the full ML lifecycle: from compute orchestration that runs large-scale training jobs, through experiment tracking and model registry, to GPU-backed serving in production.
 
 
 The Data &amp; AI Platform team owns the foundation underneath kaiko's training and serving workloads: the orchestration and distributed compute layer (Dagster + Ray on Kubernetes), the storage layer (Hammerspace), and the observability that keeps them reliable. We support both research, with large-scale training workloads on open-weight MoE models in the hundreds-of-billions to trillion-parameter range, and product, with the engineering teams building kaiko's clinical applications.
 
 
 Today, our platform is built around large-scale training. The next frontier is closing the loop: building the model lifecycle layer (experiment tracking, registry, versioning, and GPU-backed serving) that lets our trained models move reliably into product. You'll be part of the team that gets us there.
 
 
 You'll work closely with both research and product engineering to understand what the platform needs to deliver, and own the engineering of how. You'll contribute to the platform's evolution across both training and serving dimensions, helping translate team needs into reliable, scalable infrastructure.
 
 
 You will be based in either The Netherlands or Switzerland, with the expectation of spending at least 50% of your time at the office.
 
 
 
 Some areas of responsibility
 
 
 
 <ul>
 <li>
 Build and evolve the infrastructure that makes ML development fast, reliable, and observable: from IaC to CI/CD to Kubernetes-based workload orchestration.
 </li>
 <li>
 Contribute to the compute orchestration layer: help scale GPU workloads across heterogeneous on-prem and cloud clusters using Kubernetes, Ray, and advanced GPU scheduling technologies such as KAI-Scheduler.
 </li>
 <li>
 Support hybrid and multi-cloud strategies that balance performance, compliance, and cost, including the Hammerspace storage rollout.
 </li>
 <li>
 Own and evolve parts of the AI Factory: the Dagster-based orchestrator and its Ray integration that training jobs depend on.
 </li>
 <li>
 Build and maintain the model lifecycle layer: experiment tracking, model registry, versioning, and GPU-backed serving infrastructure, so models trained on our clusters move reliably into production.
 </li>
 <li>
 Partner with both research and product engineering to understand platform requirements and translate them into shared infrastructure that works across use cases.
 </li>
 <li>
 Bring engineering rigour to a fast-moving stack: lineage, reproducibility, ownership boundaries, and documentation that lets the team move quickly without losing track.
 
 
 </li>
 </ul>
 
 About You
 
 
 
 <ul>
 <li>
 2-5 years of experience in production ML platform engineering or ML Ops role.
 </li>
 <li>
 Hands-on experience with Kubernetes, Helm, Terraform, Docker, and CI/CD tooling (ArgoCD, GitHub Actions, or comparable). Comfortable with modern observability stacks (Grafana, Prometheus, Loki, or similar).
 </li>
 <li>
 Compute orchestration at scale. Production experience scheduling GPU workloads on Kubernetes, Ray, or comparable, or strong motivation to grow into this quickly.
 </li>
 <li>
 ML infrastructure exposure. Hands-on experience with Linux and NVIDIA GPU environments, including multi-node training stacks and the networking that connects them (InfiniBand or comparable). You have a feel for how researchers and engineers use the platform, not just how it's built.
 </li>
 <li>
 Familiarity with the full ML workflow: training runs, experiment tracking, model registry and versioning (MLflow or equivalent), and model serving. You understand how models move from training to production.
 </li>
 <li>
 Solid software engineering. Python as a baseline; openness to picking up Go or other programming languages where the platform demands it.
 </li>
 <li>
 Collaborative by default. You work across team boundaries (research, product, data engineering) and design interfaces that serve multiple stakeholders without requiring everyone to understand the underlying plumbing.
 
 
 </li>
 </ul>
 
 Nice to have:
 
 
 
 <ul>
 <li>
 Direct experience supporting large-scale foundation-model training or inference, with a working sense of how a run behaves from the platform's perspective: MFU, GPU utilisation, communication overhead, and dataloader stalls on the training side; serving frameworks (vLLM, Triton, TorchServe), KV cache management, and batching strategies on the inference side.
 </li>
 <li>
 Familiarity with lower-level GPU communication and I/O concepts: RDMA, GPUDirect Storage (GDS), GPUDirect RDMA, NCCL, and how these affect training throughput and cluster design.
 </li>
 <li>
 Experience with Kubernetes-native scheduling stacks for accelerator-heavy workloads, such as Volcano, KAI Scheduler, YuniKorn, or similar, so scheduler tradeoffs are understood rather than defaulted to.
 </li>
 <li>
 High performance (parallel) filesystems that feed GPU's with high throughput and low latency (Hammerspace, CEPH, WEKA, VAST).
 </li>
 <li>
 On-prem and hybrid GPU environments, especially in regulated settings (healthcare, finance, public sector).
 </li>
 <li>
 Exposure to MoE architectures or large-scale distributed training, useful for anticipating what future workloads will demand of the platform.
 
 
 </li>
 </ul>
 We are excited to gather a broad range of perspectives in our team, as we believe it will help us build better products to support a broader set of people. If you&rsquo;re excited about us but don&rsquo;t fit every single qualification, we still encourage you to apply: we&rsquo;ve had incredible team members join us who didn&rsquo;t check every box!
 
 
 
 Why kaiko
 
 
 
 At kaiko, we believe the best ideas come from collaboration, ownership and ambition. We&rsquo;ve built a team of international experts where your work has direct impact. Here&rsquo;s what we value:
 
 
 <ul>
 <li>
 Ownership: You&rsquo;ll have the autonomy to set your own goals, make critical decisions, and see the direct impact of your work.
 </li>
 <li>
 Collaboration: You&rsquo;ll have to approach disagreement with curiosity, build on common ground and create solutions together.
 </li>
 <li>
 Ambition: You&rsquo;ll be surrounded by people who set high standards for themselves and others, who see obstacles as opportunities, and who are relentless in their work to create better outcomes for patients.
 
 
 </li>
 </ul>
 
 
 
 In addition, we offer:
 
 
 
 <ul>
 <li>
 An attractive and competitive salary, a good pension plan and 25 vacation days per year.
 </li>
 <li>
 Great offsites and team events to strengthen the team and celebrate successes together.
 </li>
 <li>
 A EUR 1000 learning and development budget to help you grow.
 </li>
 <li>
 Autonomy to do your work the way that works best for you, whether you have a kid or prefer early mornings.
 </li>
 <li>
 An annual commuting subsidy.
 
 
 </li>
 </ul>
 
 Our interview process
 
 
 
 Our interview process is designed to assess mutual fit across skills, motivation, and values. It typically includes the following steps:
 
 
 <ul>
 <li>
 Screening call: A short conversation to align on your motivation, career goals, and initial fit for the role.
 </li>
 <li>
 Coding assessment &amp; debrief: You'll complete a time-limited coding exercise, started at a time of your choosing. Afterwards, you'll join a live session with members of our team to walk through your solution, explain your reasoning, and discuss any trade-offs you made.
 </li>
 <li>
 Technical interview: A deep dive into your problem-solving approach through a technical challenge, case study, or role-specific scenario.
 </li>
 <li>
 Onsite meeting (optional): You&rsquo;ll meet team members across functions to explore collaboration dynamics, team fit, and day-to-day context.
 </li>
 <li>
 Final executive conversation: A discussion with a member of the executive team focused on long-term alignment, cultural fit, and shared expectations for impact.
 </li>
 </ul>
</div>

ML Platform Engineer

Harnham

<div>
 
 
 MLOps Engineer
 
 
 
 
 Amsterdam, Netherlands (hybrid working)
 
 
 
 
 Up to 107k annually + benefits
 
 
 
 
 
 
 This is an opportunity to work on large scale, real time machine learning systems that directly impact millions of digital transactions. You will join a growing ML platform team focused on delivering robust, production ready solutions across payments and fraud, with strong ownership and visibility from day one.
 
 
 
 
 
 
 The Company
 
 
 
 They are a global technology organisation operating at the intersection of data, payments, and digital experiences. Their platform supports high volume, real time transactions for well known international businesses, focusing on performance, reliability, and innovation. With a strong engineering culture, they invest heavily in data and machine learning to drive continuous improvement. Teams are collaborative, fast paced, and committed to delivering meaningful impact.
 
 
 
 
 
 
 The Role
 
 
 
 You will focus on building and scaling MLOps capabilities within a production environment, supporting a range of machine learning use cases.
 
 
 
 
 <ul>
 <li>
 Design and build scalable systems for training, deploying, and monitoring machine learning models
 </li>
 <li>
 Support real time and batch ML workflows across critical platform services
 </li>
 <li>
 Develop and scale feature store capabilities for both online and offline use cases
 </li>
 <li>
 Improve observability and monitoring across ML systems
 </li>
 <li>
 Collaborate closely with data scientists and engineers to productionise models
 </li>
 <li>
 Take ownership of end to end delivery of ML features and systems
 </li>
 <li>
 Contribute to best practices in MLOps, CI CD, and cloud infrastructure
 </li>
 </ul>
 
 
 
 
 
 Your Skills and Experience
 
 
 <ul>
 <li>
 Strong commercial experience in MLOps or machine learning engineering
 </li>
 <li>
 High proficiency in Python for production level systems
 </li>
 <li>
 Experience deploying and maintaining machine learning models in production environments
 </li>
 <li>
 Knowledge of monitoring, observability, and system reliability
 </li>
 <li>
 Exposure to cloud platforms, ideally AWS
 </li>
 <li>
 Familiarity with ML tools and frameworks such as TensorFlow, PyTorch, Spark, or similar
 </li>
 <li>
 Experience with tools such as Databricks, SageMaker, Kubeflow, or equivalent platforms is beneficial
 </li>
 <li>
 Strong communication skills with the ability to work across technical teams
 </li>
 <li>
 A curious mindset and a practical approach to solving complex problems
 </li>
 </ul>
 
 
 
 
 
 What They Offer
 
 
 <ul>
 <li>
 Competitive salary and benefits package
 </li>
 <li>
 Hybrid working model with a collaborative office environment in Amsterdam
 </li>
 <li>
 Opportunities to work on high impact, large scale ML systems
 </li>
 <li>
 Clear progression and career development within a growing team
 </li>
 <li>
 Supportive engineering culture with strong mentorship and ownership
 </li>
 </ul>
 
 
 
 
 
 How to Apply
 
 
 
 If you are interested in this Machine Learning Ops Engineer opportunity in Amsterdam, please apply with your CV for immediate consideration.
 
</div>

Senior MLOps Engineer

Optics11

<div>
 We are expanding our team with a
 
 Data &amp; ML Infrastructure Engineer
 
 who will take ownership of the platform that enables data ingestion, processing, storage, and machine learning workflows across multiple products. Operating at the intersection of data science, software engineering, and cloud infrastructure, you will build the foundations and ensure that our ML systems are scalable, reproducible, and production ready.
 
 
 In this role, you will design, implement, and continuously improve a cloud-agnostic data and ML platform that supports multiple products and teams. You will be responsible for platform reliability, scalability, and cost-efficiency, while providing data scientists and ML engineers with the tools and infrastructure to efficiently develop, train, validate, and deploy machine learning models. You will serve as a technical bridge between the data science and IT/infrastructure teams, translating requirements into robust and maintainable systems.
 
 
 This role includes ownership of an existing vendor-delivered platform and its evolution into a fully automated, reproducible, and multi-product ML platform.
 
 
 Key Responsibilities:
 
 
 <ul>
 <li>
 Design, implement, and maintain the data and ML platform infrastructure, including systems for data ingestion, storage, processing, and training.
 </li>
 <li>
 Ensure high availability, reliability, and uptime of the platform, as it is critical to the business value proposition.
 </li>
 <li>
 Maintain and evolve vendor-delivered platform components and ensure long-term maintainability and independence.
 </li>
 <li>
 Build and maintain data workflows, including tools for dataset versioning, experiment tracking, and model lifecycle management across multiple products.
 </li>
 <li>
 Enforce DevOps / MLOps practices, including CI/CD pipelines, Infrastructure-as-Code, and automated workflows.
 </li>
 <li>
 Develop cloud-agnostic and containerized solutions that can run across public and private cloud environments within EU constraints.
 </li>
 <li>
 Optimize data storage, lifecycle policies, and cost efficiency, including tiered storage and retention strategies.
 </li>
 <li>
 Implement and maintain monitoring and alerting systems (e.g., Prometheus, Grafana) for pipelines, infrastructure, and resource usage.
 </li>
 <li>
 Ensure data governance, security, and compliance, including access control, audit logging, and anonymization requirements.
 </li>
 <li>
 Collaborate closely with data scientists, ML engineers, and IT teams to deliver infrastructure that meets operational needs.
 </li>
 <li>
 Manage resource and cost controls, including budgets, approvals, and tracking.
 </li>
 <li>
 Contribute to documentation, operational runbooks, and onboarding materials for the platform.
 
 
 
 </li>
 </ul>
 
 Why Join Us?
 
 
 
 <ul>
 <li>
 Join one of Europe&rsquo;s most promising deep-tech scale-ups.
 </li>
 <li>
 Shape the future of a rapidly growing multidisciplinary R&amp;D organization.
 </li>
 <li>
 Competitive salary and benefits package.
 </li>
 <li>
 Enjoy regular team activities and company events.
 </li>
 <li>
 Fresh team lunches provided3 times/ week.
 
 
 
 </li>
 </ul>
 Required Qualifications:
 
 
 <ul>
 <li>
 MSc or equivalent with 5+ years&rsquo; industry experience in Computer Science, Data Engineering, Machine Learning, or a related field.
 </li>
 <li>
 Strong experience with data platforms, cloud infrastructure, and distributed systems (AWS, GCP, Azure).
 </li>
 <li>
 Proven experience with Infrastructure-as-Code (e.g., Terraform) and automated deployments.
 </li>
 <li>
 Experience with ML platforms and tools (MLflow, Feast, DVC, DataHub or equivalent).
 </li>
 <li>
 Experience with AWS Sagemaker or Google Vertex AI platforms.
 </li>
 <li>
 Experience with databases (MongoDB, PostgreSQL, InfluxDB or equivalent).
 </li>
 <li>
 Experience with message brokers and streaming systems (Kafka, RabbitMQ or equivalent).
 </li>
 <li>
 Hands-on experience with containerization (Docker/OCI) and orchestration (Kubernetes or similar).
 </li>
 <li>
 Experience with CI/CD systems (e.g., GitLab CI).
 </li>
 <li>
 Programming skills in Python.
 </li>
 <li>
 Experience with monitoring and observability tools (Prometheus, Grafana).
 </li>
 <li>
 Understanding of DevOps and MLOps practices.
 </li>
 <li>
 Ability to work across teams and translate requirements into scalable infrastructure solutions.
 
 
 
 </li>
 </ul>
 
 Nice to Have:
 
 
 
 <ul>
 <li>
 Knowledge of data governance, IAM, and security frameworks.
 </li>
 <li>
 Experience with cost optimization and resource management in cloud environments.
 </li>
 <li>
 Familiarity with multi-tenant or multi-product platform architectures.
 </li>
 <li>
 Experience working with EU data compliance constraints in energy, maritime or defense.
 
 
 
 </li>
 </ul>
 
 Security &amp; Compliance:
 
 
 
 You'll be working with clients in the defense and security sectors, obtaining a Certificate of No Objection issued by the AIVD (Dutch General Intelligence and Security Service) is mandatory. This means you will need to undergo a security screening. For more information, please refer to details on security screening.
</div>

Data & ML Infrastructure Engineer

<div>We are expanding our team with a Data &amp; ML Infrastructure Engineer who will take ownership of the platform that enables data ingestion, processing, storage, and machine learning workflows across multiple products. Operating at the intersection of data science, software engineering, and cloud infrastructure, you will build the foundations and ensure that our ML systems are scalable, reproducible, and production ready.
In this role, you will design, implement, and continuously improve a cloud-agnostic data and ML platform that supports multiple products and teams. You will be responsible for platform reliability, scalability, and cost-efficiency, while providing data scientists and ML engineers with the tools and infrastructure to efficiently develop, train, validate, and deploy machine learning models. You will serve as a technical bridge between the data science and IT/infrastructure teams, translating requirements into robust and maintainable systems.
This role includes ownership of an existing vendor-delivered platform and its evolution into a fully automated, reproducible, and multi-product ML platform.<h4 class="jobSectionHeader">
Key Responsibilities:</h4><ul><li>
Design, implement, and maintain the data and ML platform infrastructure, including systems for data ingestion, storage, processing, and training.</li><li>
Ensure high availability, reliability, and uptime of the platform, as it is critical to the business value proposition.</li><li>
Maintain and evolve vendor-delivered platform components and ensure long-term maintainability and independence.</li><li>
Build and maintain data workflows, including tools for dataset versioning, experiment tracking, and model lifecycle management across multiple products.</li><li>
Enforce DevOps / MLOps practices, including CI/CD pipelines, Infrastructure-as-Code, and automated workflows.</li><li>
Develop cloud-agnostic and containerized solutions that can run across public and private cloud environments within EU constraints.</li><li>
Optimize data storage, lifecycle policies, and cost efficiency, including tiered storage and retention strategies.</li><li>
Implement and maintain monitoring and alerting systems (e.g., Prometheus, Grafana) for pipelines, infrastructure, and resource usage.</li><li>
Ensure data governance, security, and compliance, including access control, audit logging, and anonymization requirements.</li><li>
Collaborate closely with data scientists, ML engineers, and IT teams to deliver infrastructure that meets operational needs.</li><li>
Manage resource and cost controls, including budgets, approvals, and tracking.</li><li>
Contribute to documentation, operational runbooks, and onboarding materials for the platform.</li></ul><h4 class="jobSectionHeader">
Why Join Us?</h4><ul><li>
Join one of Europe&rsquo;s most promising deep-tech scale-ups.</li><li>
Shape the future of a rapidly growing multidisciplinary R&amp;D organization.</li><li>
Competitive salary and benefits package.</li><li>
Enjoy regular team activities and company events.</li><li>
Fresh team lunches provided3 times/ week.</li></ul><h4 class="jobSectionHeader"> 
Required Qualifications:</h4><ul><li>
MSc or equivalent with 5+ years&rsquo; industry experience in Computer Science, Data Engineering, Machine Learning, or a related field.</li><li>
Strong experience with data platforms, cloud infrastructure, and distributed systems (AWS, GCP, Azure).</li><li>
Proven experience with Infrastructure-as-Code (e.g., Terraform) and automated deployments.</li><li>
Experience with ML platforms and tools (MLflow, Feast, DVC, DataHub or equivalent).</li><li>
Experience with AWS Sagemaker or Google Vertex AI platforms.</li><li>
Experience with databases (MongoDB, PostgreSQL, InfluxDB or equivalent).</li><li>
Experience with message brokers and streaming systems (Kafka, RabbitMQ or equivalent).</li><li>
Hands-on experience with containerization (Docker/OCI) and orchestration (Kubernetes or similar).</li><li>
Experience with CI/CD systems (e.g., GitLab CI).</li><li>
Programming skills in Python.</li><li>
Experience with monitoring and observability tools (Prometheus, Grafana).</li><li>
Understanding of DevOps and MLOps practices.</li><li>
Ability to work across teams and translate requirements into scalable infrastructure solutions.</li></ul>
Nice to Have:<ul><li>
Knowledge of data governance, IAM, and security frameworks.</li><li>
Experience with cost optimization and resource management in cloud environments.</li><li>
Familiarity with multi-tenant or multi-product platform architectures.</li><li>
Experience working with EU data compliance constraints in energy, maritime or defense.</li></ul>
Security &amp; Compliance:
You'll be working with clients in the defense and security sectors, obtaining a Certificate of No Objection issued by the AIVD (Dutch General Intelligence and Security Service) is mandatory. This means you will need to undergo a security screening. For more information, please refer to details on security screening.</div>

Xebia

<div>
 
 
 Does this sound like you?
 
 
 
 You know the difference between ML and AI. You understand that scale is not trivial. You like to code. You know how to leverage the latest AI coding tools (like Copilot, Cursor, or Claude) to be more effective without compromising on quality. You excel at bridging the gap between data scientists and engineers to build production-ready, scalable applications driven by data and AI.
 
 
 
 
 
 
 Why Xebia Data?
 
 
 
 You'll join a team that lives and breathes knowledge sharing &ndash; think monthly hackathons, bi-weekly knowledge sharing sessions with dinner, a big development budget, and unlimited conference access. You'll work with cutting-edge AI tooling on challenging projects, surrounded by experts who are genuinely invested in your growth.
 
 
 We're hiring for people who want to further develop themselves in
 
 the following roles
 
 :
 
 
 
 
 
 
 AI Engineer
 
 
 
 You combine ML knowledge with software engineering rigor to develop cutting-edge AI or ML applications. You're responsible for taking these solutions into production to generate value &ndash; from traditional ML models to GenAI solutions and agentic AI systems that transform how businesses operate. You know how to rigorously evaluate your solutions to ensure they deliver real business value, not just impressive demos. You ensure applications are scalable, testable, robust, and extendable. To do this you work closely with data scientists, engineers and business stakeholders.
 
 
 
 
 
 
 AI Platform Engineer
 
 
 
 You build ML platforms and set the standards that enable AI and ML teams to work efficiently. You make it easy for others to deploy and maintain their solutions at scale. You establish best practices and create the infrastructure that empowers the entire team. You know and love to play with the best and newest observability tooling, staying ahead of the curve to ensure our systems are transparent, reliable, and easy to debug.
 
 
 
 
 
 
 How you will help in creating value with Data &amp; AI
 
 
 
 At Xebia Data, we solve real business problems with AI. Over the past decade, our team has predicted daily demand for millions of loaves of bread, built advanced energy-saving algorithms at a nationwide scale, automated video labeling with GenAI for TV programs, and set up data innovation labs at large multinational corporations.
 
 
 
 
 
 You'll be part of a team that transforms promising concepts into production-ready solutions that drive measurable business outcomes. Whether you're deploying ML models at scale, building the platforms that enable AI teams to work efficiently, or creating cutting-edge GenAI applications, your work will directly contribute to making our clients truly AI-driven.
 
 
 
 
 
 We've already enjoyed great success &ndash; just think of what more we can achieve when YOU join our team!
 
 
 
 
 
 
 What you will do to make AI-driven applications
 
 
 
 You collaborate closely with data scientists and business users on your team to understand the problem being addressed and their approach. Your communication skills help find the shortest path from Data to value. Your Engineering skills enable you to build scalable AI solutions that are robust and easy to maintain.
 
 
 During on-site client engagements, you may encounter challenging problems. Fortunately, Xebia organizes regular remote work sessions where you can discuss and find solutions with the collective intelligence of the entire team. If immediate assistance is needed, every Xebia member is just a quick message away, always ready to provide support.
 
 
 
 
 
 
 
 
 
 THIS IS YOUR DREAM JOB IF&hellip;
 
 
 
 ✅ You like working on the overlap between AI and software or data engineering.
 
 
 ✅ You learn quickly and have an entrepreneurial mindset.
 
 
 ✅ You like working for different clients and enjoy the excitement of regularly getting to learn new environments and know new people.
 
 
 ✅ You speak Dutch fluently.
 
 
 ✅ You love working with modern tech like CI/CD pipelines, Infrastructure-as-Code, cloud platforms, and cutting-edge GenAI/LLM integration.
 
 
 
 
 
 
 THIS IS NOT YOUR DREAM JOB IF&hellip;
 
 
 
 ❌ You only want to build models from data and are not interested in doing the work involved in taking these models into production.
 
 
 ❌ You are just fine with spaghetti code, or you do not understand what that is.
 
 
 ❌ You do not like coding, scripting, or templating.
 
 
 ❌ You do not like to explain your work and share your knowledge with your peers.
 
 
 ❌ You do not live in the Randstad.
 
 
 
 
 
 
 WHAT IT IS LIKE TO BE A CONSULTANT AT XEBIA DATA
 
 
 
 As a consultant at Xebia Data, you will have projects of varying lengths. Some are 2 weeks, some are 1 year, and most are in between. Every project is different, as is every client. We, however, always deliver the best quality possible. It is not always easy, but it is always rewarding. You will be exposed to different companies, problems, systems, and solutions. Join our dynamic team and collaborate with industry leaders such as Ahold, ASML, and Sligro! Explore the exciting projects we&rsquo;ve undertaken by diving into our inspiring customer stories.
 
 
 
 
 
 You will be surrounded by experts in different fields, and you will learn so many new things in a short time. Xebia Data offers the perfect environment to shine as a data professional.
 
 
 
 
 
 You will never be alone on this journey. We have all experienced imposter syndrome, had a difficult project, or felt down at times. We know what it takes, we know it is difficult sometimes, and we will be there for you when you need it. We say People First, and we mean it.
 
 
 
 
 
 
 Xebia Data offers you great benefits too, of course!
 
 
 
 We work in a hybrid model &ndash; at client sites, in our own office in Amsterdam Oostenburg, and from home. While flexibility is important, coming to the office is essential for building the close-knit team we are. We value team cohesion, knowledge and personal development, innovation, and fun. We strive for authority in everything we do. At most companies, you'll see similar statements, but at Xebia Data, we back this up with concrete benefits:
 
 
 
 
 <ul>
 <li>
 Monthly Hackathons
 </li>
 </ul>
 
 Full-day hackathons on company time. You can choose to work on whatever and with whomever. Want to invite a friend from outside? No problem.
 
 <ul>
 <li>
 Development Budget
 </li>
 </ul>
 
 &euro;5,000 per year and 6 days of dedicated development time &ndash; it's your obligation to spend it on anything that will make you a better data professional.
 
 <ul>
 <li>
 Conferences and Speaking
 </li>
 </ul>
 
 Attending and speaking at relevant conferences doesn't come out of your vacation time.
 
 <ul>
 <li>
 Knowledge Sharing
 </li>
 </ul>
 
 Bi-weekly knowledge sharing events with your Xebia colleagues that include dinner.
 
 <ul>
 <li>
 Sandbox Environments ☁️
 </li>
 </ul>
 
 Build, test, and experiment with anything you want in our GCP, AWS, and Azure sandbox environments.
 
 <ul>
 <li>
 A modern and dog-friendly office in Amsterdam, Oostendam. 🐶
 </li>
 <li>
 Great Community
 </li>
 </ul>
 
 We're heavily involved in our community by sponsoring, hosting, and organizing events. Have an idea for a meetup we should sponsor? Or want to start a new one? Let's do it!
 
 <ul>
 <li>
 Physical and Mental Well-being
 </li>
 </ul>
 
 Great benefits for your physical and mental health, plus free healthy lunches and not-so-healthy drinks/snacks at our offices.
 
 <ul>
 <li>
 Tech and Mobility Budget
 </li>
 </ul>
 
 A generous tech budget, plus a monthly mobility budget to spend on leasing a car, leasing the e-bike of your dreams, or getting an NS Business Card.
 
 <ul>
 <li>
 Unlimited Book Budget
 </li>
 </ul>
 
 Feed your curiosity and keep learning &ndash; order any books you want, on us.
 
 <ul>
 <li>
 Very Good Pension Plan
 </li>
 </ul>
 
 We've got your future covered with a solid pension plan.
 
 <ul>
 <li>
 Fun events and activities like ski trips, BBQs, cooking workshops, and more! 👩🏽 🍳
 </li>
 </ul>
 
 
 
 
 Our core values are
 
 people first
 
 ,
 
 quality without compromise
 
 ,
 
 knowledge sharing,
 
 and
 
 customer intimacy
 
 . At Xebia Data, you will find like-minded colleagues who are at the forefront of their field and have the courage to go off the beaten track; they are the people who make organizations move.
 
 
 
 
 
 Sounds interesting? Let&rsquo;s talk.
 
</div>

AI Engineer

reddit

<div><div><div><div><div><div><div><div>Reddit is a community of communities. It's built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 126 million daily active unique visitors, Reddit is one of the internet's largest sources of information. For more information, visit www.redditinc.com.</div>
</div>
</div></div></div></div></div>Location: Reddit has a flexible first workforce. Don't live near our office? No worries: you can work remotely from anywhere in the UK or the Netherlands.

Team Overview

We're building a scalable feature platform that powers Ads ML by making high-quality features and training datasets easy to build, share, and maintain. Our small but growing team works on projects like batch &amp; realtime feature management platform, training set generation platform, sequence features platform and, agentic and automated ML workflows for feature lifecycle management.

We are looking for an engineer with experience in building high-scale data infrastructure and exposure to ML platforms to help evolve and scale our feature management systems.

This is not a pure ML modeling role. The ideal candidate is excited about building reliable infrastructure, data pipelines, and developer-facing tools that make ML engineers more productive.

What You'll Do
<ul>
<li>Design and build data infrastructure that supports large-scale feature and training set computation, transformation, and storage.</li>
<li>Develop frameworks for batch and real-time features with a focus on reliability, scalability, and ease of use.</li>
<li>Build platform capabilities for feature governance, including lineage tracking, validation, drift detection, anomaly monitoring, reproducibility, and versioning</li>
<li>Partner with ML engineers to ensure smooth integration of feature engineering workflows into ML production systems.</li>
<li>Build systems that support agentic ML workflows, including automated feature discovery, feature quality evaluation and feature lifecycle management</li>
<li>Contribute to operational excellence through observability, performance tuning, reliability engineering, and cost optimization initiatives.</li>
</ul>
What You Bring
<ul>
<li>3+ years in data infrastructure/platform engineering or ML infrastructure platforms.</li>
<li>Hands-on experience building production services, data pipelines, APIs, workflow systems, or developer tools.</li>
<li>Experience with at least one distributed data or compute system such as Spark, PySpark, Flink, Kafka, Ray, Airflow, Kubernetes, BigQuery, or similar technologies.</li>
<li>Familiarity with ML data workflows such as feature generation, training dataset creation, batch processing, real-time data processing, model training, experimentation, or online serving.</li>
<li>Strong coding skills and ability to write clean, maintainable, well-tested code.</li>
<li>Experience building intelligent automation or agentic workflows for ML systems is a strong plus</li>
<li>Experience with ML infrastructure and MLOps workflows spanning feature engineering, training pipelines, experimentation, model deployment, and online serving is a plus</li>
</ul>
Benefits:
<ul>
<li>Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support</li>
<li>Family Planning Support</li>
<li>Gender-Affirming Care</li>
<li>Mental Health &amp; Coaching Benefits</li>
<li>Private Pension plan with Employer-matching</li>
<li>100% employer-sponsored group medical plan</li>
<li>Income Replacement Programs</li>
<li>Flexible Vacation &amp; Paid Volunteer Time Off</li>
<li>Generous Paid Parental Leave</li>
</ul><div>
In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.

During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable. We will not sell your personal information or disclose it to any third party for their marketing purposes. We will delete any recording of your interview promptly after making a hiring decision. For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors.

Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve. Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.</div></div>

Machine Learning Systems Engineer, Ads ML Platform

Arrows

<div>
 
 
 Data Engineer | Amsterdam | Cyber Security x AI
 
 
 
 
 
 
 I'm working with an early-stage deep-tech company building an
 
 AI-powered platform for safety-critical and industrial environments
 
 . They are hiring a
 
 Senior Data &amp; Cloud Engineer
 
 to join the team as they scale their core platform.
 
 
 
 
 
 This is a hands-on role with
 
 genuine ownership over architecture
 
 and product direction, working closely with an experienced founding team that has successfully built and exited companies before.
 
 
 
 
 
 
 What you'll be doing:
 
 
 <ul>
 <li>
 
 Building and scaling a cloud-native data
 
 and AI platform across AWS and on-prem environments
 </li>
 <li>
 Designing and maintaining streaming and batch pipelines
 </li>
 <li>
 Working with
 
 industrial data sources
 
 including SCADA, historians, and telemetry
 </li>
 <li>
 Supporting
 
 real-time analytics
 
 and ML pipelines
 </li>
 <li>
 Handling
 
 Kafka, ETL, and data lakes/warehouses
 
 </li>
 </ul>
 
 
 
 
 
 What's on offer:
 
 
 <ul>
 <li>
 Hybrid setup (Amsterdam based)
 </li>
 <li>
 Salary up to &pound;120,000 plus bonus plus equity
 </li>
 <li>
 High impact role with real ownership and autonomy
 </li>
 <li>
 Backed by a highly experienced leadership team with a strong track record
 </li>
 <li>
 Opportunity to help shape a genuinely novel product in a fast-growing space
 </li>
 </ul>
 
 
 
 
 If this sounds interesting, feel free to share your CV.
 
</div>

Data Engineer

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