Intermediate Applied Machine Learning Scientist - Cancer Care AI Center of Excellence (COE) Sie[...]

Join us in pioneering breakthroughs in healthcare. For everyone. Everywhere. Sustainably. Our inspiring and caring environment forms a global community that celebrates diversity and individuality. We encourage you to step beyond your comfort zone, offering resources and flexibility to foster your professional and personal growth, all while valuing your unique contributions. Intermediate Applied Machine Learning Scientist – Cancer Care AI Center of Excellence (COE) Siemens Healthineers Role Summary The Intermediate Applied Machine Learning Scientist will design, develop, and deploy deep learning solutions for oncology imaging, with a strong emphasis on 3D medical image segmentation, domain adaptation, and adaptive radiotherapy workflows. This role focuses on translating cutting‑edge AI research into clinically valuable tools that support radiation oncologists and improve precision in cancer care using CBCT, CT, and multimodal data. Key Responsibilities: Model Development & Technical Innovation Develop and optimize 3D deep learning models for auto‑segmentation of critical structures in cancer imaging on CBCT, CT, and other modalities, leveraging frameworks such as nnU‑Net, TotalSegmentator, MedSAM, and similar tools. Implement domain adaptation, transfer learning, pseudo‑label fusion, and synthetic‑to‑real techniques to improve model performance across multi‑vendor clinical datasets. Build scalable pipelines for medical image preprocessing, exploratory data analysis, feature extraction, landmark detection (heatmap regression), and high‑quality ground‑truth dataset curation in collaboration with clinicians. Clinical Integration & Validation Advance adaptive radiotherapy applications, including treatment response prediction, dose‑volume histogram analysis, and clinical validation using metrics such as Dice Similarity Coefficient (DSC) and AUROC. Ensure models meet regulatory, safety, and performance standards through rigorous experimentation and multi‑site validation. Collaborate with radiation oncologists, radiologists, and cross‑functional teams to translate clinical needs into robust, production‑ready AI solutions. Research & Collaboration Contribute to publications, patents, and knowledge sharing while staying current with advances in medical AI. Work closely with clinical partners to refine tools based on user feedback and real‑world performance. Support dataset harmonization and annotation pipelines to enable high‑impact oncology AI development. Knowledge, Skills & Experience The ideal candidate is a hands‑on ML researcher with strong expertise in medical imaging and a passion for oncology applications. M.Sc. (or PhD) in Computer Science, Computing Science, Biomedical Engineering, Medical Imaging, or a related field. 1–3 years of applied deep learning experience in medical imaging, preferably in oncology, adaptive radiotherapy, or 3D segmentation. Strong proficiency in Python, PyTorch (TensorFlow/Keras a plus), nnU‑Net workflows, medical imaging libraries (MONAI, SimpleITK), and tools such as TotalSegmentator and MedSAM. Experience curating clinical datasets, performing domain adaptation, and conducting rigorous validation (Dice, bootstrap CIs, etc.). Familiarity with DICOM data, multi‑vendor CT systems (e.g., Siemens), and collaboration with clinical teams. Publications or demonstrated impact in medical AI preferred. Excellent communication and cross‑functional collaboration skills. Technical & Leadership Competencies 3D Medical Imaging Expertise: Deep understanding of CBCT/CT segmentation, landmark detection, and adaptive radiotherapy pipelines. Technical Excellence: Strong problem‑solving in domain adaptation, transfer learning, and production‑oriented ML workflows. Clinical Focus: Ability to deliver solutions that provide measurable value to oncologists and patients. Collaboration & Communication: Works effectively with clinicians, engineers, and researchers; translates complex concepts for diverse stakeholders. Innovation & Rigor: Balances cutting‑edge research with practical, validated clinical outcomes. Integrity & Quality: Committed to responsible AI, patient safety, and regulatory excellence. Compensation $67,000 - $95,500. Factors which may affect starting compensation within this range may include geography/market, skills, education, experience, and other qualifications of the successful candidate. Benefits Siemens Healthineers offers a variety of health and wellness benefits including a flexible benefits plan, Defined Contribution Pension Plan, Registered Retirement Savings Plan matching contributions, and a competitive paid time off program including vacation, company holidays, sick leave, and parental leave (all subject to eligibility requirements). Equal Employment Opportunity Statement Siemens Healthineers is committed to creating a diverse environment and is proud to be an equal opportunity employer. While we appreciate all applications we receive, we advise that only candidates under consideration will be contacted. #J-18808-Ljbffr Siemens Healthineers