Senior AI Researcher (Healthcare AI)

**Location: Texas
Department: Artificial Intelligence / Healthcare R&D

Employment Type: Full-Time

About the Role

We are seeking a visionary and highly accomplished Senior AI Researcher to lead pioneering research at the intersection of Artificial Intelligence and Healthcare. In this role, you will bridge the gap between advanced algorithmic breakthrough and real-world clinical application.
You will be responsible for defining and executing a high-impact research agenda, solving complex biomedical problems, and establishing our organization as a thought leader by publishing regularly in world-class peer-reviewed venues, with a particular focus on the IEEE ecosystem (e.g., IEEE J-BHI, TMI, EMBC) and top-tier AI conferences (NeurIPS, CVPR, KDD).

Key Responsibilities

• Core Research & Novelty: Design, develop, and implement novel AI architectures, machine learning frameworks, and deep learning algorithms tailored to healthcare challenges (e.g., multimodal data fusion, computer vision for medical imaging, generative AI, LLMs for electronic health records, or explainable AI).


• High-Impact Publication: Serve as the lead or senior author on peer-reviewed manuscripts. Drive the end-to-end publication pipeline from conceptualization and experimental design to paper drafting, formatting, and navigating the peer-review cycle for elite IEEE journals, transactions, and international conferences.


• Problem Solving & Clinical Deployment: Translate ambiguous, messy, and large-scale clinical, biological, and health informatics datasets into highly accurate, generalizable, and ethically sound predictive models.


• Intellectual Leadership: Mentor junior AI scientists, engineers, and research interns. Stay at the cutting edge of AI literature and regularly present findings at global conferences and internal stakeholders.


• Cross-Functional Collaboration: Partner with clinical advisory boards, data engineers, and product teams to ensure research methodologies align with strict healthcare regulations (HIPAA, FDA software pathways) and real-world clinical workflows.

Required Qualifications & Experience

• Education: Ph.D. in Computer Science, Biomedical Engineering, Electrical Engineering, Data Science, or a related quantitative field with a heavy focus on Machine Learning/AI.


• Track Record: A strong and verifiable portfolio of first-authored publications in top-tier journals or conferences—specifically IEEE Transactions/Journals (J-BHI, TMI, TPAMI), IEEE EMBC/ISBI, or premier AI venues (NeurIPS, ICML, ICLR, CVPR).


• Technical Mastery: Advanced proficiency in Python and deep learning frameworks (PyTorch or TensorFlow). Experience working with distributed computing, high-performance GPU clusters, and handling complex cloud-based AI environments.


• Domain Expertise: Deep understanding of healthcare data modalities (such as DICOM images, EHR clinical notes, time-series signals like ECG/EEG, or genomic sequences) and standard open benchmark datasets (e.g., MIMIC).


• Communication: Exceptional communication skills with the ability to translate highly technical AI concepts to clinical practitioners and business executives alike.

Preferred Qualifications

• Experience implementing Explainable AI (XAI) frameworks or developing clinical decision support systems optimized for physician interpretability.


• Prior experience dealing with data privacy-preserving architectures like Federated Learning in a healthcare context.


• Active involvement or membership in professional technical societies such as the IEEE Engineering in Medicine and Biology Society (EMBS) or the IEEE Computer Society.