Principal Scientist – Biomedical Machine Learning

Boehringer Ingelheim

Principal Machine Learning Scientist designing and developing sophisticated biomedical models for drug discovery at Boehringer Ingelheim. Collaborating with international teams in a hands-on role focused on AI solutions.

Posted 7/24/2026full-timeVienna • 🇦🇹 AustriaLead💰 €90,900 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Machine Learning and Deep Learning, particularly in the context of biomedical data, with a strong focus on model evaluation, interpretability, and collaboration with domain experts. Proficient in developing scalable modeling frameworks and applying advanced techniques to derive clinically meaningful insights.

Highest-signal resume keywords

PhD In Computer ScienceMachine Learning ExpertiseDeep Learning ExpertisePython ProficiencyExperience With Biomedical Data

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningDeep LearningFine-TuningTransfer LearningModel EvaluationData NormalizationBatch EffectsLabel GenerationData Quality AssessmentScientific Rigor

Soft Skills

Critical ThinkingCollaborationCommunication

Tools & Technologies

PyTorchCloud-Based ML PlatformsVersion Control

Industry Keywords

Computational BiologyBiomedical DataDigital PathologyMedical ImagingTranscriptomicsOmics DataComputational PathologyMultimodal AI

Tech Stack

Tools & technologies

CloudPythonPyTorch

About the role

Key responsibilities & impact

Build, fine-tune, and benchmark Machine Learning and Deep Learning models on biomedical data, with an initial focus on adapting pathology foundation models (e.g., Prov-GigaPath, Virchow, and H-Optimus-0) as well as multimodal imaging and omics models for downstream predictive tasks.
Collaborate closely with computational biologists, oncologists, and pathologists to translate complex biological questions into well-defined machine learning problems and convert model outputs into biologically and clinically meaningful insights.
Embed rigorous evaluation, validation, and interpretability throughout the model development lifecycle, leveraging approaches such as attention- and gradient-based methods, explainability techniques, and counterfactual analyses.
Develop reusable and well-documented modeling frameworks, components, and best practices that accelerate future projects and promote scalability across the organization.
Stay at the forefront of advances in computational pathology, multimodal AI, and machine learning, identifying and applying the most relevant innovations to deliver scientific impact.

Requirements

What you’ll need

PhD in Computer Science, Data Science, Bioinformatics, Computational Biology, or another relevant scientific discipline, combined with hands-on industry experience in Machine Learning and Deep Learning at a senior level.
Deep and demonstrable expertise in Machine Learning and Deep Learning, supported by strong proficiency in Python and modern deep learning frameworks (e.g., PyTorch), as well as proven experience in fine-tuning, transfer learning, and adapting large pre-trained models.
Strong hands-on experience working with biological and biomedical data, such as transcriptomics/RNA-seq, digital pathology, medical imaging, or other omics data, including a solid understanding of domain-specific challenges such as normalization, batch effects, label generation, and data quality considerations.
Strong scientific rigor and critical thinking skills, with the ability to assess whether models are learning meaningful biological signals and to design robust evaluation strategies that withstand biological and clinical scrutiny.
Proven experience leading ML projects end-to-end, collaborating effectively with both technical and domain experts, and applying software engineering best practices, including version control, reproducibility, and development on modern cloud-based ML platforms.

Benefits

Comp & perks

Flexible working time models: home office and flexible working hours, depending on department and position – many things are possible with us.
Additional days off (“bridge-days”): more free time through additional days off to bridge single working days between bank holidays and the weekend – without having to use vacation days.
Canteen & Cafeteria: whether it's coffee and croissant for breakfast, various lunch menus or snacks in between – our subsidized staff restaurant & cafeteria has something for every taste including vegetarian and vegan options.
Learning & development: diverse training and development opportunities for your personal and professional growth. Because: you never stop learning.
Health promotion: health is important to us – that's why we offer different programs to promote physical and mental health.
Public transport ticket: we encourage our employees to use public transport on their daily way to work. Costs for public transport? We cover them!