Clinical Ink is seeking an AI Engineer to build next-generation AI capabilities for clinical trial technology, enhance digital health platforms, support data-driven decision-making, and improve operational efficiency.
Requirements
- 2+ years of experience in applied machine learning or AI engineering.
- Proficiency in Python and ML libraries (e.g., scikit-learn, PyTorch, TensorFlow, XGBoost).
- Experience building and validating machine learning models on real-world datasets.
- Strong understanding of supervised and unsupervised learning techniques.
- Experience with cloud platforms (e.g., AWS, Azure) and MLOps workflows.
- Experience with time-series data or biomedical sensor data (e.g., CGMs, wearables) preferred.
- Familiarity with NLP techniques for text classification, summarization, or LLM applications preferred.
Responsibilities
- Build and train machine learning models for applications such as data quality prediction, sensor signal interpretation, language models for eCOA optimization, and trial participant behavior analysis.
- Collaborate with product and engineering teams to integrate AI-powered features into web and mobile applications used by trial participants, clinicians, and sponsors.
- Rapidly prototype AI solutions and proof-of-concept applications to evaluate feasibility and business value.
- Clean, transform, and analyze structured and unstructured clinical trial data, wearable data, and patient-reported outcomes to support model training and evaluation.
- Contribute to internal AI tools that automate or optimize internal operations such as quality control, documentation review, or software test prioritization.
- Work with DevOps and engineering to deploy models in production environments and monitor model performance over time.
- Work in an Agile environment with cross-functional teams.
Other
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field.
- Strong problem-solving skills and attention to detail.
- Exposure to clinical trial processes, health tech, or eCOA systems preferred.
- Experience working with healthcare data formats (e.g., HL7 FHIR, CDISC, JSON APIs) preferred.
- Document technical approaches, assumptions, and results clearly for both technical and non-technical audiences.
