Software Engineer, Hippocratic AI

• 5-8 years of experience in building software applications for large-scale distributed data systems
• Strong engineering background with experience in infrastructure and/or distributed systems, with proficiency in Python, Java, or similar languages
• Solid experience with ETL processes and data pipeline design, ensuring high-quality data for ML model development and deployment
• Familiarity with the complete software development life cycle, from design and implementation to testing and deployment
• Proven track record in building and maintaining high-availability, low-latency systems with a focus on reliability, testing, and observability
• Pragmatic approach to problem-solving, knowing when to aim for ideal solutions and when to adjust course
• Experience with big data technologies such as Apache Spark for data processing and large-scale data analytics
• Strong sense of curiosity and a collaborative mindset, eager to learn new technologies and share knowledge within the team

Desirable

• This role at Hippocratic AI is focused on building and optimizing the data infrastructure that powers our machine learning (ML) operations, including our generative AI models and large language models (LLMs) for healthcare
• You will design and scale reliable, data-driven services for ML model training, data processing, and deployment, ensuring our Research Scientists can seamlessly transition from experimentation to production
• This role involves working with massive datasets, managing ETL pipelines, and building scalable solutions to handle data ingestion, transformation, and storage across Hippocratic AI’s systems
• Build powerful, flexible, and user-friendly data and ML infrastructure that supports all ML Speech and LLM operations across Hippocratic AI
• Design and develop fast, reliable data services for ML model training, ETL pipelines, and deployment, scaling infrastructure across multiple regions
• Create services and libraries that enable ML engineers to move efficiently from data experimentation to production, especially for generative AI models
• Collaborate closely with product teams, data engineers, and research scientists to develop data-focused infrastructure that supports production-ready generative AI and LLM/ Multimodal models for healthcare applications