Rice University is seeking a development engineer with business development experience to accelerate the adoption and impact of the institute's innovations by building and maintaining strategic partnerships with industry leaders and developing machine learning (ML) software and pipelines for digital health innovations.
Requirements
- Strong programming skills in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn)
- Familiarity with version control (Git) and collaborative development
- Experience with data pipelines (e.g., Airflow, Prefect, Luigi) and cloud platforms (e.g., AWS, GCP, or Azure)
- Understanding of CI/CD workflows, containerization (e.g., Docker, Kubernetes)
- Knowledge of healthcare data formats (HL7, FHIR, DICOM)
- Background in signal processing, time-series, or imaging data
- Experience deploying ML in clinical/research settings
Responsibilities
- Design, build, and maintain end-to-end ML pipelines for data ingestion, preprocessing, training, validation, and deployment
- Collaborate with data scientists, clinicians, and engineers to translate research models into scalable production systems
- Develop APIs, microservices, and cloud-based deployment frameworks to support ML applications
- Ensure reproducibility, scalability, and compliance with healthcare data standards (HIPAA, FDA guidance)
- building and maintaining machine learning (ML) software and pipelines that power digital health innovations
- Lead collaborations with external partners, including industry, academic institutions, and healthcare providers, to further enhance the development and implementation of data-driven healthcare solutions
- Manage complex research projects, including timelines, deliverables, and coordination with internal and external stakeholders
Other
- 2+ years of experience in digital health commercialization and business development with industry partners
- Working with potential customers on product specifications
- Managing timelines
- Ensuring successful delivery
- A proven track record of industry experience in digital health and commercialization is essential, with the ability to translate research discoveries into commercially viable products and services for patient care
