Software Engineer, Cleerly

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related field (or equivalent experience)
• 5–8 years of software engineering experience, with expertise in AI production systems (Python, PyTorch) and data services (SQL, Postgres, NoSQL, Redis or similar)
• Demonstrable capability in designing, implementing, and securing RESTful web services
• Experience in MLOps orchestration and cloud deployment (Docker, Kubernetes)
• Experience with AWS, GitHub, and continuous integration pipelines
• Proven track record of delivering scalable systems in production environments
• Strong systems thinking with the ability to evaluate tradeoffs in scalability, reliability, and maintainability
• Experience designing for observability and operational excellence (e.g., logs, alerts, dashboards, runbooks)
• Comfortable operating in ambiguity and driving clarity across product, engineering, and business stakeholders

Desirable

• We're seeking a Software Engineer 3 to drive key technical initiatives across our computational imaging pipeline
• In this high-impact role, you'll be part of the team owning the complex systems responsible for deploying, scaling, and monitoring our regulated AI algorithms end-to-end
• You will influence the architecture of our system, ensuring our solutions meet medical standards for quality and performance, while mentoring other engineers and helping shape our technical culture
• You will join our expert Computational Imaging team, where we develop sophisticated algorithms to enhance and improve medical imaging
• We are focused on improving the quality and clinical value of medical images, creating powerful, extra software components that correct artifacts, standardize data, and ensure the utmost precision before the final analysis
• Our work is essential for elevating the reliability and accuracy of Cleerly’s regulated products
• Build and deploy scalable AI services and computational imaging pipelines to production, ensuring robust, high-availability infrastructure for our machine learning algorithms
• Contribute to the system architecture's design and make pragmatic trade-offs that balance performance, security, and maintainability
• Own full lifecycle delivery of complex features, from architectural planning and API design to implementation and post-release observability
• Support the high availability and observability of our services; improve alerting, logging, and incident response across the stack
• Uphold strong engineering standards via code reviews, technical mentorship, and design documentation
• Collaborate closely across AI and engineering to reduce knowledge silos and ensure continuity in critical systems through cross-training and documentation
• Implement robust testing mechanism and validation strategies to ensure compliance with our Quality Management System and regulatory standards