Data ML Engineering Lead

ppkaiko.ai Amsterdam, North Holland, Netherlands /ph3About the role /h3pKaiko’s Multimodal Large Language Model (MLLM) is trained on domain-specific, high-complexity medical data. To reach clinical-grade performance, we’ll need to ramp up our data efforts to manage massive scale, ensure consistent quality, and tightly control data relevance and integrity. /ppbAs our ML Data Engineering Lead /b, you’ll directly drive this frontier. You’ll own a significant portion of our data roadmap and steer the technical direction of our ingestion, generation, and filtering pipelines. You’ll work closely with the ML research lead to align infrastructure with research priorities, design scalable architectures, and prioritize experiments that increase our data leverage. /ppThis is bboth a managerial and hands-on position /b, which means you’ll drive technical execution on core data systems while coaching your team and enabling your reports to grow. /ppYou will be based in Zurich or Amsterdam, with the expectation of spending at least 3 days a week at the office. /ph3About you /h3ullibYou’ve led technical teams /b before: hiring, coaching, and developing engineers to do their best work. /lilibYou’ve owned technical direction /b for large-scale data pipelines, making long-term architectural decisions around ingestion, synthetic data generation, and filtering that aligned with company and research priorities. /liliYou have deep experience bdesigning and operating large-scale data pipelines /b, including synthetic data generation for LLMs, data-quality experimentation, and core data primitives like hashing, deduplication, and chunking. /liliYou still bhave excellent programming skills /b in Python and deep experience with distributed frameworks such as Ray or Spark. /li /ulpbNice to have: /b /pulliHands‑on production experience orchestrating complex DAGs in Dagster (preferred) or similar workflow engines. /liliExpertise in data‑quality validation frameworks and monitoring/observability tooling. /liliStrong grasp of machine‑learning fundamentals (model architectures, training paradigms, evaluation metrics) to collaborate deeply with researchers and guide data‑driven choices. /li /ulpWe 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’re excited about us but don’t fit every single qualification, we still encourage you to apply: we’ve had incredible team members join us who didn’t check every box! /ppAt kaiko, we believe the best ideas come from collaboration, ownership and ambition. We’ve built a team of international experts where your work has direct impact. Here’s what we value: /pullibOwnership /b: You’ll have the autonomy to set your own goals, make critical decisions, and see the direct impact of your work. /lilibCollaboration /b: You’ll have to approach disagreement with curiosity, build on common ground and create solutions together. /lilibAmbition /b: You’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 /ulpIn addition, we offer: /pulliAn attractive and competitive salary, a good pension plan and 25 vacation days per year. /liliGreat offsites and team events to strengthen the team and celebrate successes together. /liliA EUR 1000 learning and development budget to help you grow. /liliAutonomy to do your work the way that works best for you, whether you have a kid or prefer early mornings. /liliAn annual commuting subsidy. /li /ulpOur interview process is designed to assess mutual fit across skills, motivation, and values. It typically includes the following steps (though it will be more extensive for leadership positions): /pullibScreening call: /b A short conversation to align on your motivation, career goals, and initial fit for the role. /lilibTechnical interview: /b A deep dive into your problem-solving approach through a technical challenge, case study, or role-specific scenario. /lilibOnsite meeting: /b You’ll meet team members across functions to explore collaboration dynamics, team fit, and day-to-day context. /lilibFinal executive conversation: /b A discussion with a member of the executive team focused on long-term alignment, cultural fit, and shared expectations for impact. /li /ulh3Seniority level /h3ulliDirector /li /ulh3Employment type /h3ulliFull-time /li /ulh3Job function /h3ulliIndustries: Technology, Information and Internet and Hospitals and Health Care /li /ulpOur team values collaboration and initiative. This role is open to candidates who can contribute to building better products to support patients and researchers. /p /p #J-18808-Ljbffr