Responsibilities
- Create and manage robust, scalable pipelines for collecting, processing, and storing data, emphasizing reliability, monitoring, and thorough testing.
- Build tools, frameworks, and automated solutions to enhance development speed while maintaining system safety and integrity.
- Construct comprehensive AI and machine learning pipelines spanning data intake, preprocessing, model training, experimentation, deployment, and integration with downstream platforms such as EHR modules, microservices, and reporting dashboards.
- Enable ongoing model refinement and ensure reliable production performance through monitoring for accuracy, data drift, bias, fairness, and reproducibility.
- Promote a collaborative engineering culture centered on learning, knowledge exchange, and mentoring fellow team members.
Responsibilities
- Design, build, and maintain scalable systems for ingestion, transformation, and storage of data, with a focus on testing and observability.
- Implement frameworks, tooling, and automation to safely increase development velocity.
- Develop foundational end-to-end AI/ML workflows from (1) source ingestion and preparation, (2) training and tuning, (3) experimentation and productionization, and (4) downstream systems integration (EHR modules, micro-services, dashboards).
- Support iterative model development and production operations and observability (accuracy, drift, bias, fairness, reproducibility).
- Contribute to a culture of continuous improvement, knowledge-sharing and mentoring of peer engineers.
Other
- Prolonged periods sitting at a desk and working on a computer.
- Must be able to frequently communicate with others through virtual meeting applications such as Zoom and Google Meet.
- Must be able to observe and communicate information on company provided laptop.
- Move up to 10 pounds on occasion.
- Must be eligible to work in the United States without sponsorship now or in the future.
Not eligible for sponsorship now or in the future
