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Hippocratic AI is building a healthcare-focused large language model (LLM) designed to transform patient outcomes at scale. The company's AI agents are already deployed in real hospitals and health systems. As a Senior Software Engineer in Research, you will architect and own the backend infrastructure powering these AI agents, ensuring systems operate reliably at production scale with 99.9%+ uptime while supporting exponential growth.
Key responsibilities include designing and building scalable backend systems that reliably handle high-volume healthcare data and LLM processing. You will develop efficient data pipelines that ingest, process, and prepare large-scale healthcare datasets for AI training and inference with minimal latency. You'll create high-performance APIs and microservices enabling seamless data retrieval and AI model interaction, reducing processing time and improving system responsiveness.
You will monitor and optimize backend systems for performance and reliability, implementing monitoring that catches issues before they impact production AI agents. You'll build infrastructure enabling reproducible ML workflows from data preparation through model deployment, accelerating time-to-production for new AI capabilities. Additional responsibilities include developing and optimizing backend infrastructure supporting data ingestion, feature extraction, and tagging workflows, and implementing tag management and metadata systems to streamline dataset organization and retrieval.
Collaboration is central to the role: you'll work closely with data scientists, ML engineers, product managers, and physicians to understand healthcare requirements and translate them into technical solutions. The position requires 5 days weekly in-person collaboration in Menlo Park.
Required qualifications include a Bachelor's degree in Computer Science or related field (Master's preferred) and 4+ years of backend development experience using Python, Golang, or similar languages. You must have experience building and maintaining multi-modal data pipelines (speech, vision, text) using Ray or Apache Airflow, and distributed computing experience with Spark or Hadoop. Familiarity with relational databases, RESTful APIs, and cloud infrastructure (AWS, GCP) is essential.
Preferred qualifications include exposure to AI/ML concepts or LLM experience, experience with sensitive or regulated data, familiarity with gRPC or GraphQL, real-time audio experience, and DevOps exposure (CI/CD, deployment, Terraform, build systems).
