Senior AI Engineer

• Model Development & Deployment - Develop, train, and deploy ML models for biomarker detection, risk stratification, longitudinal monitoring, and decision-support systems for screening and survivorship.
• ML Infrastructure - Build and maintain scalable pipelines for data ingestion, feature engineering, training, and model serving on cloud platforms (e.g., AWS, GCP).
• Data Engineering - Collaborate with clinical and data teams to curate and manage high-quality, multi-modal datasets (e.g., genomics, imaging, EHRs).
• Research to Production - Turn advanced research into real-world systems, including adapting foundation models and integrating multi-modal learning into clinical workflows.
• Evaluation & Monitoring - Implement evaluation frameworks, experimentation pipelines, and monitoring tools to ensure robust, clinically reliable performance.
• Collaboration - Partner with clinicians, researchers, product managers, and engineers to align technical solutions with healthcare needs.

• MSc or PhD in computer science, machine learning, computational biology, or a related field
• Proven experience building and deploying ML models in production
• Strong Python skills and experience with frameworks like PyTorch, JAX, or TensorFlow
• Familiarity with cloud GPUs/TPUs, modern ML pipelines, and containerization (e.g., Docker)
• Solid software engineering practices (version control, testing, CI/CD, code review)
• Ability to communicate complex concepts to technical and non-technical audiences
• Experience with healthcare or biomedical data (e.g., genomics, pathology, EHRs)
• Publications or open-source contributions in relevant fields
• Startup or entrepreneurial experience, especially in fast-paced environments
• Experience building ML systems from zero to one

The compensation package contains a base salary, bonus, equity and a comprehensive benefits package.

Harnham is partnering with a well-funded biotech startup that is developing an AI-driven approach to more personalized cancer monitoring-integrating proprietary methods, diverse datasets, and evolving diagnostic capabilities to support ongoing care for individuals with elevated risk. The team brings experience across genomics and health data modeling, applying these foundations toward new screening and recommendation approaches, with a focus on translating research into practical clinical applications.