Machine Learning Engineer

Role Overview

Machine Learning Engineer responsible for the end-to-end ML work behind CoreViva’s imaging products. You will translate fuzzy clinical questions into deployable models, ship them for radiologist use, and build the surrounding production and evaluation systems.

Responsibilities

• Own ML development end to end: from clinical question → modeling decisions → shipped model
• Develop medical imaging models for segmentation and predictions on whole-body MRI data
• Extend capabilities toward agentic/LLM-like solutions to support scaling
• Build and maintain evals and MLOps (e.g., monitoring and deployment workflows)
• Stay close to radiologists, translating clinical insights into technical requirements
• Design production systems around models and ensure reliable deployment

Requirements

• Hands-on experience building and deploying ML models in production (ideally medical imaging)
• Strong modern imaging ML knowledge, including segmentation architectures (e.g., 2D/3D U-Net) and metrics such as Dice and Hausdorff
• MLOps fundamentals: data versioning, experiment tracking, model registry, drift monitoring, and containerized deployments
• Product builder mindset: comfortable making product decisions and shipping used features
• Ability to work through early-stage ambiguity: scope vague problems and drive to completion
• Strong communication skills and ability to work directly with clinicians/founders and a small engineering team

Nice to Have

• Experience with DICOM/NIfTI pipelines and medical imaging frameworks such as MONAI
• Familiarity with MRI imaging sequences (e.g., T1, T2, Dixon)
• Prior experience as an early engineer at a startup
• Agentic-AI/LLM experience
• Streaming ASR or speech-to-text experience

Compensation

• $120k–$180k + equity