Software Engineer, HeartFlow

• Bachelor's/Master’s degree in Computer Science, Engineering, or a related field
• 2+ years of relevant job experience in Software Engineering with hands-on experience with cloud-based distributed data systems
• Strong foundation in software engineering principles and practices, with proficiency in Python and SQL
• Deep understanding of modern distributed data cloud architectures for structured and unstructured data
• Experience with distributed computing frameworks (e.g. Ray/Spark/Dask) and supporting infrastructure (e.g. Hadoop, Docker, Kubernetes)
• Competency with at least one cloud provider (e.g. AWS, GCP, Azure)
• Experience with infrastructure as code (CDK, Terraform)
• Experience constructing and maintaining data products for technical stakeholders
• Excellent communication and interpersonal skills, with the ability to communicate to both technical and non-technical audiences

Desirable

• We seek a highly motivated and dynamic Software Engineer who is passionate about supporting ML algorithm development through data systems and MLOps
• You will work closely with our experienced engineers and researchers to design, build, and maintain the infrastructure and tools necessary for rapid ML development, deployment, and monitoring
• You will gain hands-on experience with the latest technologies in data systems, MLOps and cloud computing, contributing to projects that impact our products and services, and ultimately, move the needle on heart disease
• The ideal candidate is passionate about using first-principles thinking to navigate challenging problems in data systems & ML infrastructure
• Develop robust ETL (Extract, Transform, Load) processes to integrate data from diverse sources into our data ecosystem
• Design and maintain scalable data pipelines that provide our teams with high-quality, training-ready datasets
• Implement and manage tools to track and document data lineage, from source to consumption
• Empower consumers of data products through detailed documentation
• Develop and maintain a large-scale distributed computing platform for ML algorithm training and evaluation
• Develop and maintain a standardized approach to ML algorithm experiment tracking using tools like MLFlow
• Work cross-functionally with Researchers and Engineers to understand their needs for ML algorithm training and production monitoring