Machine Learning Engineer
Job Description
About Nucs AI
Nucs AI is revolutionizing cancer care through cutting-edge AI and medical imaging technology. Founded in 2024 by a multidisciplinary team of oncologists, AI researchers, and healthcare innovators, we’re tackling one of medicine’s most pressing challenges: the growing demand for accurate, timely cancer diagnostics in the face of rising scan volumes and limited radiologist capacity.
We sit at the intersection of diagnostics and treatment planning—building AI-powered tools at the convergence of medical imaging, radioligand therapy, and artificial intelligence. Starting with prostate cancer and expanding across oncology, we partner with world-leading medical institutions and pharmaceutical companies across the US, Europe, and Australia to bring precision oncology into everyday clinical practice.
Our mission is to enhance diagnostic precision and expand access to expert-level cancer care, improving patient outcomes worldwide. We’re venture-backed, early-stage, and building a team that blends deep clinical expertise with engineering intensity—moving with the rigor the medical field demands and the speed the problem deserves.
The Opportunity
As an ML Engineer at Nucs AI, you will be at the forefront of developing and deploying cutting-edge machine learning models to power our AI-driven medical software solutions. Your role will involve building algorithms that analyze medical images and data, driving innovation in prostate cancer diagnosis and treatment. You will work closely with our engineering teams to ensure that our AI models are scalable, secure, and aligned with clinical and regulatory standards.
What You'll Build
Model Development: Design, train, and optimize machine learning models for medical image analysis, leveraging deep learning techniques (e.g., CNNs, Vision Transformers, Multi-Modal LLMs).
Data Processing: Build data pipelines to clean, preprocess, and augment large datasets of medical images, ensuring the highest accuracy of AI models.
Integration: Collaborate with back-end developers to integrate AI models into our software solutions for seamless deployment in clinical environments.
Cloud-Based AI: Develop scalable AI solutions on cloud platforms (Google Cloud preferred) using tools such as PyTorch, ONNX Runtime or similar.
Regulatory Compliance: Ensure AI models and data processing adhere to healthcare regulations like HIPAA and meet medical industry standards.
Evaluation Pipeline: Build strong evaluation pipelines to extract insights about trained model performance.