Thank you for your interest in joining Solventum. Solventum is a new healthcare company with a long legacy of solving big challenges that improve lives and help healthcare professionals perform at their best. At Solventum, people are at the heart of every innovation we pursue. Guided by empathy, insight, and clinical intelligence, we collaborate with the best minds in healthcare to address our customers’ toughest challenges. While we continue updating the Solventum Careers Page and applicant materials, some documents may still reflect legacy branding. Please note that all listed roles are Solventum positions, and our Privacy Policy: https://www.solventum.com/en-us/home/legal/website-privacy-statement/applicant-privacy/ applies to any personal information you submit. As it was with 3M, at Solventum all qualified applicants will receive consideration for employment without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
Job Description:
The Opportunity
We are looking for an AI Scientist to build and scale the next generation of AI-driven healthcare solutions. In this role, you will be a key contributor to our Healthcare Information System (HIS) applications, moving beyond experimental notebooks to build real-world, secure, and scalable AI/ML applications.
You will work across the full spectrum of AI—from Classical ML and Deep Learning to exploring Generative and Agentic AI—ensuring our tools are reliable and transformative for clinicians and patients.
Key Responsibilities
1. Model Development & Deployment
- Build & Iterate: Design, train, and fine-tune ML models using a mix of classical statistical methods and modern deep learning architectures.
- Full-Cycle Execution: Assist in the end-to-end process of taking models from data cleaning and feature engineering to deployment in production environments.
- Generative AI Integration: Implement and evaluate LLM-based components and agentic workflows to enhance system reasoning and natural language capabilities.
2. Data Science & Engineering
- Clinical Data Handling: Process and analyze structured and unstructured healthcare data (e.g., FHIR, clinical notes, claims) to extract actionable insights.
- Evaluation & Testing: Conduct rigorous testing to ensure model accuracy, fairness, and safety within a highly regulated healthcare context.
- Performance Tuning: Optimize models for latency and throughput to meet the demands of real-time healthcare applications.
3. Collaboration & Growth
- Technical Partnership: Work independently or closely with other scientists and engineers to translate research requirements into production-ready code.
- Documentation: Maintain clear documentation of experiments, model architectures, and data pipelines to ensure reproducibility.
Candidate Profile
Education & Experience:
- Master’s in Computer Science, AI, Machine Learning, or a related field (or a Bachelor’s with 5+ years of relevant industry experience).
- Hands-on Experience: At 3+ years of experience developing and deploying AI/ML models in a professional or intensive academic setting.
Core Technical Stack:
- ML Fundamentals: Solid foundation in Classical ML (Regression, Clustering, Tree-based models) and Deep Learning (Neural Networks, Transformers).
- GenAI Awareness: Experience with (or strong interest in) Generative AI frameworks, RAG systems, and prompt engineering.
- Programming: Proficiency in Python and standard ML libraries (PyTorch, TensorFlow, Scikit-learn, Hugging Face).
Bonus Points (Preferred):
- MLE/MLOps: Familiarity with Git, Docker, or basic MLOps workflows.
- Cloud Basics: Exposure to cloud platforms like AWS, GCP or Azure.
- Healthcare Focus: Understanding of medical data privacy (HIPAA) or healthcare data formats.
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