Job Description

A cutting-edge research team is looking for a highly motivated Machine Learning Engineer to help transform the future of clinical trial design through innovative applications of artificial intelligence. This role sits within an interdisciplinary group focused on merging advanced machine learning techniques with complex biological and clinical data to shape the next generation of healthcare solutions.

This is a unique opportunity to contribute to high-impact, high-visibility projects where your work can directly influence the success of clinical programs. The team’s focus spans multimodal generative modeling, representation learning, and reinforcement learning—making it an ideal environment for those who thrive at the intersection of theory and application. Your contributions may lead to publications in top-tier venues and, more importantly, meaningful advances in patient outcomes.

Key Responsibilities:

• Design and implement deep learning algorithms for medical image analysis (e.g., semantic segmentation, instance detection).
• Collaborate with interdisciplinary teams, including imaging scientists, clinical experts, and machine learning researchers, to integrate AI solutions into clinical decision-making workflows.
• Analyze complex multimodal datasets to uncover actionable insights for trial design and drug development.
• Stay current with advances in machine learning and healthcare AI, and bring new ideas into active development.
• Contribute to academic and industry publications to share innovations with the broader scientific community.

Basic Qualifications:

• M.S. (or equivalent experience) in Computer Science, Machine Learning, Bioengineering, or a related quantitative discipline.
• Demonstrated success applying ML models in real-world research or production settings.
• Strong programming proficiency in Python and hands-on experience with one or more major ML frameworks (e.g., PyTorch, TensorFlow, JAX).
• Experience with MLOps practices, including code versioning, use of HPC environments, and tracking ML experiments.
• Proven ability to build, scale, and maintain machine learning pipelines.

Preferred Experience:

• Familiarity with medical imaging modalities such as OCT, CFP, MRI, CT, X-ray, or PET.
• Experience with image model architectures such as UNet, DeepLab, ViT, SAM, or Detectron.
• Exposure to tools like Weights & Biases for experiment tracking and model monitoring.

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