Key skills
Machine learning model building and deploymentMedical imaging MLSegmentation architectures (2D/3D-UNETs)Dice metricHausdorff metricMLOps fundamentalsData versioningExperiment trackingModel registryDrift monitoringContainerized deploymentsDICOM pipelinesNIFTI pipelinesMONAI frameworkMRI imaging sequences T1MRI imaging sequences T2MRI imaging sequences DIXONProduct builder mentalityComfort with ambiguityAIMachine LearningMLLLMAgenticMLOpsCommunication
About this role
CoreViva is a whole-body MRI startup building AI tools that help radiologists work faster and give patients clearer answers. They are seeking a Machine Learning Engineer to own the ML side of their imaging work, transforming clinical questions into deployable models and designing the necessary production systems.
Responsibilities:
- You'll own the ML side of our imaging work end to end, transforming fuzzy clinical questions to a model that gets shipped and used by radiologist as well as designing the production systems around it
- The work spans medical imaging models for segmentation and predictions on our whole-body MRI data to more agentic solutions that will help us scale as well as the evals and MLOps that surround them
- You'll stay close to the radiologist who depend on what you ship and translate clinical insights into modeling decisions
Requirements:
- Hands on experience building and deploying ML models in production (ideally in medical imaging)
- Depth in modern imaging ML: segmentation architectures (2D/3D-UNETs, etc), Dice, Hausdorff
- Experience with MLOPs fundamentals: data versioning, experiment tracking, model registry, drift monitoring, containerized deployments
- Product builder mentality: comfortable making product calls and shipping things that get used
- Comfortable with early-stage ambiguity: takes a vague problem, scopes it, and drives it completion
- Strong communication: work directly with the clinicians/founders, and a small engineering team
- Experience with DICOM/NIFTI pipelines and medical imaging frameworks (MONAI)
- Familiarity with MRI imaging sequences (T1, T2, DIXON, etc)
- Prior experience as an early engineer at a startup
- Agentic-AI/LLM experience
- Streaming ASR or speech-to-text experience