Machine Learning Engineer (Model Dev)

ArteraAI is an AI startup focused on developing medical artificial intelligence tests to personalize therapy for cancer patients. As a Machine Learning Engineer, you will develop AI-based biomarkers and work on deep-learning models to predict patient risk and response to therapy, collaborating with various teams to impact patient care.

Responsibilities:

• Develop and evaluate AI-based biomarkers using multimodal data
• Design, implement, and improve machine-learning models to predict patient outcomes and treatment response
• Contribute to the end-to-end model development lifecycle, including data preparation, training, evaluation, and validation
• Support the productionalization, launch, and monitoring of machine-learning models in collaboration with platform and product teams
• Conduct research and experimentation to improve model performance, robustness, generalizability, and interpretability
• Collaborate with biostatistics, clinical, and product partners to translate clinical questions into machine-learning solutions
• Contribute to scientific publications and conference submissions alongside the broader research team

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
• Develop and evaluate AI-based biomarkers using multimodal data
• Design, implement, and improve machine-learning models to predict patient outcomes and treatment response
• Contribute to the end-to-end model development lifecycle, including data preparation, training, evaluation, and validation
• Support the productionalization, launch, and monitoring of machine-learning models in collaboration with platform and product teams
• Conduct research and experimentation to improve model performance, robustness, generalizability, and interpretability
• Collaborate with biostatistics, clinical, and product partners to translate clinical questions into machine-learning solutions
• Contribute to scientific publications and conference submissions alongside the broader research team
• 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