<div class="show-more-less-html__markup show-more-less-html__markup--clamp-after-5 relative overflow-hidden"> <p><strong>Location: Berlin, Germany</strong></p><p><strong>Start Date: As soon as possible / by arrangement </strong></p><p><br/></p><p><strong>ABOUT DAiNA</strong></p><p><br/></p><p>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.</p><p><br/></p><p>For more information, visit: www.daina.com </p><p><br/></p><p><strong>THE ROLE</strong></p><p><br/></p><p>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.</p><p><br/></p><p><strong>WHAT YOU’LL DO</strong></p><p><br/></p><p>• Design, build and operate applied AI/ML systems for DAiNA’s precision-oncology platform</p><p>• Develop and maintain retrieval-augmented generation (RAG) architectures for molecular data interpretation, clinical reporting, literature/knowledge retrieval and internal decisionsupport workflows.</p><p>• Build robust components for document ingestion, chunking, embedding, vector search, reranking, grounding, citation handling and evaluation.</p><p>• 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.</p><p>• Evaluate and fine-tune domain-specific models where this adds measurable value, using reproducible training, validation and benchmarking workflows.</p><p>• Integrate AI components into DAiNA’s reporting layer, data platform and broader computational oncology workflows.</p><p>• Develop guardrails, audit trails, hallucination-control strategies and human-in-the-loop review mechanisms for sensitive scientific and clinical use cases.</p><p>• Lead and coordinate a small distributed technical team or external specialists across architecture, retrieval, model serving, fine-tuning and AI evaluation.</p><p>• Work closely with bioinformatics, data engineering, clinical, wet-lab and management stakeholders to ensure AI systems address real workflow needs.</p><p><br/></p><p><strong>WHAT YOU BRING</strong></p><p><br/></p><p>• Strong software engineering background with hands-on experience building productiongrade ML, AI or LLM-based systems.</p><p>• Practical experience with large language models, including open-weight model families</p><p>• Deep practical understanding of RAG systems, including embeddings, vector databases, chunking strategies, retrieval, re-ranking, grounding and evaluation.</p><p>• Experience with MLOps practices such as model/version management, automated evaluation, monitoring, logging, deployment and reproducibility.</p><p>• Strong communication skills and the ability to explain technical trade-offs clearly to scientific, clinical and management stakeholders.</p><p><br/></p><p><strong>NICE TO HAVE </strong></p><p><br/></p><p>• Experience in healthcare, biotech, diagnostics, pharma or another regulated environment.</p><p>• Experience with clinical or biomedical data, including molecular profiles, omics data, pathology reports, clinical notes, literature, treatment guidelines or real-world evidence.</p><p>• Familiarity with digital twin approaches, computational oncology, cancer biology or personalized medicine.</p><p><br/></p><p><strong>WHY DAINA</strong></p><p><br/></p><p>• Employer contributions toward private health insurance or supplementary health coverage, as well as pension or retirement savings.</p><p>• Hybrid model with ~60% of working time expected on-site and the rest remotely, depending on team and business needs.</p><p>• Performance-based bonus opportunity, depending on company and individual performance.</p><p>• A personal development budget for conferences, courses, certifications, and training.</p><p>• The opportunity to work directly on real patient cases and help shape a first-in-class precision-oncology platform</p><p>• A proactive, collaborative team with fast decision-making and strong ownership of your domain.</p><p><br/></p><p><strong>HOW TO APPLY</strong></p><p><br/></p><p><strong>Send your CV and a short note on why this role fits you to recruiting@daina.com, quoting “Applied AI / ML Lead Engineer” in the subject line. We review applications on a rolling basis and aim to reply quickly.</strong></p> </div>