Data ML Engineer (Medtech)

TL;DR

Data ML Engineer (Medtech): Designing and building data-sourcing, synthetic-generation, and curation pipelines for multimodal large language models with an accent on high-throughput data processing at petabyte scale. Focus on managing data quality, integrity, and relevance to achieve clinical-grade performance in diagnostic AI agents.

Location: Must be based in the Netherlands or Switzerland, with an expectation of at least 50% time in the office.

Company

hirify.global is a well-funded startup building a next-generation agentic clinical AI assistant to support clinicians in complex diagnostic workflows.

What you will do

• Design and build high-throughput data pipelines for petabyte-scale multimodal data.
• Develop and integrate synthetic data generation pipelines for LLMs.
• Implement filtering and rating mechanisms for content quality and policy compliance.
• Collaborate with ML researchers to steer the development of state-of-the-art medical models.
• Manage data-quality experiments and optimize scalability/performance trade-offs.

Requirements

• Must be based in the Netherlands or Switzerland.
• Ability to work from the office at least 50% of the time.
• Strong programming skills in Python and experience with distributed frameworks like Ray or Spark.
• Hands-on experience building large-scale data pipelines and running data-quality experiments.
• Deep familiarity with Lakehouse paradigms (Delta, Iceberg) and columnar formats.
• Experience with core data processing primitives such as hashing, deduplication, and chunking.

Nice to have

• Production experience orchestrating complex DAGs in Dagster.
• Expertise in data quality, validation frameworks, and observability tooling.
• Strong grasp of machine learning fundamentals including model architectures and evaluation metrics.

Culture & Benefits

• Competitive salary and pension plan.
• 25 days of vacation per year.
• EUR 1000 annual learning and development budget.
• Annual commuting subsidy.
• Regular team offsites and events.

Hiring process

• Screening call to align on motivation and professional goals.
• Technical interview involving a case study or role-specific scenario.
• Onsite meeting to explore team fit and collaboration dynamics.
• Final conversation focused on long-term alignment and impact.