Abrar Admin

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

<div>
 
 About Kaiko
 
 
 
 kaiko.ai is a clinical AI research lab in Zurich and Amsterdam. We train our own large-scale frontier-class models end to end, on our own compute. The full stack is in scope, from training infrastructure and post-training algorithms to data generation, evaluation, and production inference.
 
 
 Our models are developed and deployed at leading European hospitals, and that deployment feeds back into the next training cycle.
 
 
 We're hiring interns across the lab. You'll work on the real problems of training and serving these models in a regulated clinical setting.
 
 
 
 The Opportunity
 
 
 
 As an ML Research Intern, you will be embedded in our core research team for six months, working alongside PhD researchers and senior engineers on problems that directly shape our clinical foundation model. You will own real work, contribute ideas, and see your output reflected in our system.
 
 
 Depending on your background and preferences, your project may sit in one of the following areas:
 
 
 <ul>
 <li>
 Data: Synthetic clinical data generation, multimodal data pipelines, curation and quality filtering.
 </li>
 <li>
 Evaluation: Clinical benchmarks, agentic eval design, measurement infrastructure for the production stack.
 </li>
 <li>
 Scaling: Continued pre-training on clinical corpora, post-training algorithms, distributed training at frontier scale.
 </li>
 <li>
 RL: Agentic RL on real clinical workflows, reward modeling, RL infrastructure.
 </li>
 <li>
 Acceleration: Inference &amp; Training optimisation, serving stack, quantisation and throughput tuning.
 
 
 </li>
 </ul>
 You will discuss and agree on your specific focus during the interview process, based on mutual fit and current priorities.
 
 
 
 Who You Are
 
 
 
 We are looking for MSc students or recent graduates in machine learning, computer science, or a related quantitative field. We care more about sharp thinking, high agency, and genuine curiosity than a perfect CV.
 
 
 
 You should bring:
 
 
 
 <ul>
 <li>
 Strong programming in Python and comfort with ML frameworks (PyTorch or equivalent).
 </li>
 <li>
 Hands-on experience with ML research or engineering, ideally in training, post-training, RL, data, or evaluation. Coursework, thesis, internship, or open-source all count.
 </li>
 <li>
 Clear technical communication, in design docs and in discussion with researchers, engineers, and clinical scientists.
 </li>
 <li>
 Motivation to work on hard problems with real ownership. Intern projects ship into the live training and serving stack.
 
 
 </li>
 </ul>
 The specific area depends on the project. None of these are required upfront.
 
 
 
 Why kaiko
 
 
 
 <ul>
 <li>
 Work at the frontier: you will be in the room with PhD researchers and engineers who have built production ML systems at top labs and institutions. Expect to be challenged and to grow fast.
 </li>
 <li>
 Ownership: you will set goals, make decisions, and present your findings directly to the research team, not to an intern coordinator.
 </li>
 <li>
 Conversion pathway: strong interns are actively considered for full-time roles. This is how we build the team.
 </li>
 <li>
 Fair compensation: an internship salary of CHF4000/EUR3000 gross per month
 
 
 </li>
 </ul>
 
 Interview Process
 
 
 
 We keep it fast. The process typically involves three steps:
 
 
 <ul>
 <li>
 Screening call (30 min): motivation, background, and initial fit.
 </li>
 <li>
 Technical assessment: a short, focused problem relevant to your area, reviewed together in a follow-up conversation.
 </li>
 <li>
 Onsite or video interview: meet the team, go deeper on your thinking, and align on project fit.
 
 
 </li>
 </ul>
 We review applications on a rolling basis. If you are finishing your degree this summer and want to start in September, apply now.
</div>

ML Research Graduate Internship

<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
 
 
 
 Our team is tackling the challenge of building a secure, scalable data and AI platform that can process massive, multimodal datasets for training cutting-edge MLLM models - while also handling sensitive medical data directly within hospital environments.
 
 
 As a Data Platform Engineer, you'll design, deploy, maintain and optimize a self-hosted, open-source&ndash;driven infrastructure that powers data ingestion, transformation, and serving for both AI training pipelines and real-time hospital applications. Your primary focus will be on the platform itself - the infrastructure, tooling, and services that enable others to work with data effectively, rather than building data pipelines as an end goal. Your work will ensure that pipelines built on top of our platform are reproducible and compliant with strict privacy regulations.
 
 
 This is a rare opportunity to shape a platform from the ground up - working with state-of-the-art tooling, solving unique scaling and security problems, and owning key technical decisions that will directly impact groundbreaking AI models and healthcare workflows.
 
 
 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>
 Design, maintain and own infrastructure and deployment for data exploration, transformation, and storage - including orchestration, containerization, and monitoring.
 </li>
 <li>
 Build and maintain internal platform services that abstract away complexity and promote self-serve data access and processing.
 </li>
 <li>
 Manage, deploy, and champion the use of standard open-source tooling and products around data platform and data engineering - including deploying and managing services via Helm charts and provisioning resources with Terraform.
 </li>
 <li>
 Design and implement data pipelines where needed, contributing to data and ML workflows as part of broader platform ownership.
 </li>
 <li>
 Collaborate with researchers, product teams, and other stakeholders to support their data needs.
 </li>
 </ul>
 
 
 
 
 
 About You
 
 
 <ul>
 <li>
 2-5 years of experience in production data platform, infrastructure, or platform engineering roles.
 </li>
 <li>
 Experience in providing in-house data orchestration services based on open-source software such as Dagster, Airflow or Prefect.
 </li>
 <li>
 Hands-on experience with infrastructure-as-code and container orchestration - particularly Terraform for resource provisioning and Helm for deploying services to Kubernetes.
 </li>
 <li>
 Proficiency with modern storage formats (e.g., Parquet, Delta, Iceberg) and object stores (e.g., S3, MinIO, Azure Blob).
 </li>
 <li>
 Solid programming skills in Python or another language suitable for data workflows (e.g., Scala or Java).
 </li>
 <li>
 Ability to thrive in a fast-paced, startup environment with a high degree of ownership.
 </li>
 </ul>
 
 
 
 
 
 Nice to have:
 
 
 <ul>
 <li>
 Experience in AI/ML environment.
 </li>
 <li>
 Understanding of data standards in the medical domain, such as DICOM, FHIR, pathology slide images (Whole Slide Images).
 </li>
 <li>
 Experience with high performance (parallel) filesystems that feed GPU's with high throughput and low latency (Hammerspace, CEPH, WEKA, VAST).
 </li>
 <li>
 Knowledge of monitoring, logging, alerting and observability tools (e.g. Prometheus, Grafana, ELK Stack or Datadog).
 </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: 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>

Data Platform Engineer

Join kaiko.ai and explore 3   exciting career opportunities. They're hiring for various roles with options for remote or on-site work. Apply today and become part of their innovative team.

kaiko.ai

About Company

ML Platform Engineer

ML Research Graduate Internship

Data Platform Engineer