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Data ML Engineering Lead
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About kaiko
Delivering high quality cancer care is complex; specialists form a view of each patient's condition by reasoning across different data - CT scans, genomics context, treatment history and clinical notes.
Current AI are powerful within domains but fall short when it comes to reasoning across data or domain areas. kaiko.w, our AI assistant for oncology, aims to equip every clinician with a full understanding of their patients, helping them to reason across data as they assess each case.
We're building this in close collaboration with the Netherlands Cancer Institute (NKI) and a growing network of hospitals and research centers. We've raised significant long-term funding and have nearly doubled our team over the past year. We're now 80+ people representing 25 nationalities, based across our offices in Zurich and Amsterdam
About the role
Kaiko'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.
As our ML Data Engineering Lead, 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.
This is both a managerial and hands-on position, which means you'll drive technical execution on core data systems while coaching your team & enabling your reports to grow.
You will be based in Zurich or Amsterdam, with the expectation of spending at least 3 days a week at the office.
About you
- You've led technical teams before: hiring, coaching, and developing engineers to do their best work.
- You've owned technical direction for large-scale data pipelines, making long-term architectural decisions around ingestion, synthetic data generation, and filtering that aligned with company and research priorities.
- You have deep experience designing and operating large-scale data pipelines,
including synthetic data generation for LLMs, data-quality experimentation, and core data primitives like hashing, deduplication, and chunking. - You still have excellent programming skills in Python and deep experience with distributed frameworks such as Ray or Spark.
Nice to have:
- Hands‑on production experience orchestrating complex DAGs in Dagster (preferred) or similar workflow engines.
- Expertise in data‑quality & validation frameworks and monitoring/observability tooling.
- Strong grasp of machine‑learning fundamentals (model architectures, training paradigms, evaluation metrics) to collaborate deeply with researchers and guide data‑driven choices.
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'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
Why kaiko
At 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:
- Ownership: You'll have the autonomy to set your own goals, make critical decisions, and see the direct impact of your work.
- Collaboration: You'll have to approach disagreement with curiosity, build on common ground and create solutions together.
- Ambition: 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.
In addition, we offer:
- An attractive and competitive salary, a good pension plan and 25 vacation days per year.
- Great offsites and team events to strengthen the team and celebrate successes together.
- A EUR 1000 learning and development budget to help you grow.
- Autonomy to do your work the way that works best for you, whether you have a kid or prefer early mornings.
- An annual commuting subsidy.
Our interview process
Our 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):
- Screening call: A short conversation to align on your motivation, career goals, and initial fit for the role.
- Technical interview: A deep dive into your problem-solving approach through a technical challenge, case study, or role-specific scenario.
- Onsite meeting: You'll meet team members across functions to explore collaboration dynamics, team fit, and day-to-day context.
Final executive conversation: A discussion with a member of the executive team focused on long-term alignment, cultural fit, and shared expectations for impact.
