Applied AI​/ML Lead Engineer

Start Date:
As soon as possible / by arrangement

DAiNA is a precision-oncology company focused on enabling personalized cancer treatment for individual patients. We combine comprehensive molecular tumor data including genomics, transcriptomics (bulk, single cell and spatial), proteomics and epigenetics, with AI-driven analysis. Our platform connects multi-omic profiling with functional ex-vivo tumor models and personalized liquid-biopsy monitoring, creating a continuous workflow from biopsy and treatment selection through to therapy monitoring and adaptation.

We also operate GMP manufacturing to produce individualized N=1 therapeutics. In short, we help physicians make more informed, personalized treatment decisions based on high-dimensional molecular tumor data.

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As Applied AI / ML Lead Engineer, you will help build DAiNA’s applied AI capability at the core of our precision-oncology platform. Your focus will be on translating modern machine learning, large language models and retrieval-augmented generation into robust, secure and clinically relevant software systems. You will design and implement AI-driven workflows that support molecular data interpretation, clinical reporting, knowledge retrieval, decision support and digital-twin development.

A key part of the role is to ensure that sensitive patient and molecular data can be processed in a secure, compliant and auditable environment, with strong control over model behavior, data provenance and system performance.

• Design, build and operate applied AI/ML systems for DAiNA’s precision-oncology platform
• Develop and maintain retrieval-augmented generation (RAG) architectures for molecular data interpretation, clinical reporting, literature/knowledge retrieval and internal decisionsupport workflows.
• Build robust components for document ingestion, chunking, embedding, vector search, reranking, grounding, citation handling and evaluation.
• Deploy and operate open-weight and/or self-hosted models in secure cloud or isolated environments, reducing unnecessary dependency on external AI providers for sensitive patient data.
• Evaluate and fine-tune domain-specific models where this adds measurable value, using reproducible training, validation and benchmarking workflows.
• Integrate AI components into DAiNA’s reporting layer, data platform and broader computational oncology workflows.
• Develop guardrails, audit trails, hallucination-control strategies and human-in-the-loop review mechanisms for sensitive scientific and clinical use cases.
• Lead and coordinate a small distributed technical team or external specialists across architecture, retrieval, model serving, fine-tuning and AI evaluation.
• Work closely with bioinformatics, data engineering, clinical, wet-lab and management stakeholders to ensure AI systems address real workflow needs.

• Strong software engineering background with hands-on experience building production grade ML, AI or LLM-based systems.
• Practical experience with large language models, including open-weight model families
• Deep practical understanding of RAG systems, including embeddings, vector databases, chunking strategies, retrieval, re-ranking, grounding and evaluation.
• Experience with MLOps practices such as model/version management, automated evaluation, monitoring, logging, deployment and reproducibility.
• Strong communication skills and the ability to explain technical trade-offs clearly to scientific, clinical and management stakeholders.

• Experience in healthcare, biotech, diagnostics, pharma or another regulated environment.
• Experience with clinical or biomedical data, including molecular profiles, omics data, pathology reports, clinical notes,…