Help develop AI-based biomarkers that support the personalization of cancer therapy.
Work on multimodal deep-learning models that integrate large-scale pathology whole-slide images with diverse modalities to predict patient risk and response to therapy.
Contribute to developing robust, scalable solutions in this space.
Collaborate closely with fellow ML engineers, biostatisticians, scientific directors, and product partners to build, validate, and deploy models that ultimately impact patient care.

Requirements

2+ years of industry experience using PyTorch or TensorFlow.
Experience contributing to machine-learning systems deployed or maintained in production environments.
Ability to clearly communicate complex technical concepts to cross-functional, non-ML collaborators.
Experience working with large-scale image data or computer vision models.
Familiarity with self-supervised representation learning (e.g., MoCo, DINOv2) and / or vision–language models (VLMs) and multimodal representation learning.
Interest in healthcare, medical imaging, or applied machine learning in regulated or high-impact domains.

Benefits

401k matching
Unlimited paid time off (PTO)

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills

PyTorchTensorFlowmachine learningdeep learningcomputer visionself-supervised representation learningmultimodal representation learninglarge-scale image datamodel deploymentproduction environments

Soft skills

communicationcollaboration