Job Details

Applied Scientist – NLP / Deep Learning

The impact you'll make in this role

As a data scientist specializing in deep learning and medical document understanding, you will work at the forefront of healthcare AI, developing next-generation deep learning systems that understand complex clinical text. You will design, implement, optimize, train, and evaluate deep learning models in PyTorch, advancing state-of-the-art approaches in natural language understanding, representation learning, transformer architectures, and generative AI.

You will collaborate closely with clinical domain experts, fellow data scientists, ML engineers, and product teams to transition research into scalable production systems. This role requires strong mathematical intuition, hands-on deep learning experience, and the ability to rapidly evaluate, implement, and extend emerging research.

As a data scientist you will have the opportunity to tap into your curiosity and collaborate with some of the most innovative and diverse people around the world. Here, you will make an impact by:

• Scoping, designing, and developing NLP/NLU systems that support clinical understanding of medical documents and downstream healthcare workflows.
• Designing, implementing, training, and optimizing deep learning models in PyTorch, including transformer architectures, attention mechanisms, and modern neural networks for clinical text understanding.
• Developing neural representation learning approaches for medical text, including dense embeddings, semantic similarity, dense retrieval, and generative AI applications.
• Translating business and clinical requirements into mathematically sound machine learning formulations, experimental designs, and evaluation strategies.
• Processing, aligning, and enriching diverse structured and unstructured medical datasets for supervised, self-supervised, and generative learning.
• Implementing, debugging, and optimizing custom neural architectures, tensor operations, loss functions, training loops, and inference pipelines while ensuring scalable and reproducible experimentation.
• Understanding and mitigating model weaknesses, including bias, drift, hallucination behavior, robustness, calibration, and generalization.
• Designing experiments and performing rigorous error analysis to explain model behavior and communicate findings clearly to both technical and non-technical audiences.
• Deploying models into Solventum's cloud environment, partnering with ML engineering to ensure reliability, observability, scalability, and compliance within regulated healthcare systems.
• Staying current with emerging research in NLP, LLMs, representation learning, and deep learning, evaluating new methods and identifying opportunities to advance Solventum's AI initiatives.

Your skills and expertise

To set you up for success in this role from day one, Solventum requires (at a minimum) the following qualifications:

• Master's degree or PhD in computer science, mathematics, or related fields or Bachelor's degree with at least 5 years of IT experience
• Solid experience in Python especially in deep learning for text analysis and libraries such as PyTorch and Transformers
• Solid grasp of statistics and exploratory data analysis
• Experience building, training, and debugging deep learning models directly in PyTorch beyond high-level APIs.
• Strong mathematical foundation in linear algebra, optimization, probability, and machine learning.

Additional qualifications that could help you succeed even further in this role include:

• Experience conducting research-driven NLP/NLU work involving representation learning, attention mechanisms, transformer architectures, or hybrid neural networks.
• Strong understanding of neural representation learning, embedding spaces, semantic similarity, dense retrieval, or representation learning techniques.
• Experience implementing custom neural architectures, tensor operations, training loops, optimization strategies, and model debugging in PyTorch.
• Hands-on experience adapting, fine-tuning, and evaluating LLMs using modern deep learning techniques. Experience with prompting, retrieval-augmented generation (RAG), or agentic AI frameworks is a plus.
• Ability to understand, reproduce, implement, and extend ideas from contemporary deep learning and NLP research literature.
• Ability to self-organize across multiple technical and business contexts while communicating complex findings with clarity and confidence.
• Familiarity with AWS, GitHub, CI/CD, MLOps, and scalable ML deployment practices.
• Experience with ETL of large-scale text using PySpark, Spark NLP, or distributed data processing frameworks.
• Experience extracting insights from complex clinical datasets and presenting those insights to varied audiences.
• Exposure to clinical coding systems or medical terminologies (e.g., ICD, CPT, SNOMED) is a plus.

Work location:

• Remote

Relocation assistance: Not authorized

Must be legally authorized to work in country of employment without sponsorship for employment visa status (e.g., H1B status).