Develop, deploy, and maintain state-of-the-art machine learning models for medical imaging, NLP, and multimodal tasks.
Design and implement robust, scalable ML pipelines and shared infrastructure to support agile experimentation and deployment.
Collaborate with researchers to translate novel algorithms into production-ready solutions.
Build and maintain MLOps tools and practices, including automated testing, continuous integration, and monitoring of deployed models.
Optimize model performance for speed, reliability, and scalability in production environments.
Partner with clinicians, engineers, and product teams to align machine learning efforts with clinical and product needs.
Thrive in a dynamic and rewarding environment that emphasizes excellence, autonomy, and impact.

Master’s or PhD in Computer Science, Engineering, or a related field with 3+ years' experience in relevant roles. Senior and Principal roles considered based on experience.
Proficiency in Python and machine learning frameworks such as PyTorch or TensorFlow.
Strong understanding of MLOps practices, including model deployment, CI/CD pipelines, and performance monitoring.
Experience working with cloud platforms (e.g., AWS, GCP, Azure) and tools like Docker, Kubernetes, or Terraform.
Knowledge of data engineering principles, including data manipulation tools like SQL and pandas.
Familiarity with healthcare data and clinical workflows is a plus.
Exceptional problem-solving skills, ownership mindset, and a collaborative approach.

ML Engineer

Key skills