Senior Research EngineerAbout the RoleHippocratic AI's AI agents are already working inside real hospitals and health systems - this role owns the backend infrastructure that keeps them fast, reliable, and ready to scale as that footprint grows. You'll architect systems built for tomorrow's volume, not just today's, working closely with data scientists, ML engineers, and product managers to turn healthcare requirements into production-grade infrastructure.
What You'll DoBuild Scalable, Reliable AI Infrastructure- Architect backend systems that sustain 99.9%+ uptime for high-volume healthcare data and LLM processing as usage grows exponentially
- Implement monitoring that surfaces issues before they reach production AI agents
- Own performance and reliability improvements across backend systems in collaboration with data scientists and ML engineers
Engineer Data Pipelines for Healthcare AI- Design data pipelines that ingest, process, and prepare large-scale, multi-modal (speech, vision, text) healthcare datasets for training and inference with minimal latency
- Build tag management and metadata systems that make large datasets organized and retrievable
- Develop and optimize infrastructure supporting data ingestion, feature extraction, and tagging workflows
Ship APIs and ML Enablement- Develop APIs and microservices that reduce processing time and improve responsiveness for AI model interaction and data retrieval
- Build infrastructure that makes ML workflows reproducible end-to-end, from data preparation through model deployment
- Accelerate time-to-production for new AI capabilities
Location and TravelWe believe the best ideas happen together. To support fast collaboration and a strong team culture, this role is expected to be in our Menlo Park office five days a week.
What You BringMust-Have- Bachelor's degree in Computer Science, Computer Engineering, or a related field (Master's preferred)
- 4+ years of backend development experience using Python, Golang, or similar languages
- Experience building and maintaining multi-modal (speech, vision, text) data pipelines using Ray, Apache Airflow, or similar for distributed processing and model experimentation, including distributed computing frameworks like Spark or Hadoop
- Familiarity with relational database systems and RESTful APIs
- Basic understanding of cloud infrastructure (AWS, GCP, or similar)
Nice-to-Have- Exposure to AI/ML concepts or experience working with LLMs
- Experience working in teams that handle sensitive or regulated data
- Familiarity with gRPC, GraphQL, or similar
- Experience with real-time audio
- Exposure to DevOps concepts - CI/CD, deployment, Terraform, build systems
- Experience with data science
What Success Looks LikeWithin 6-12 months, the backend systems you've built or hardened are sustaining 99.9%+ uptime under exponential growth in healthcare data and LLM traffic. New AI capabilities move from data preparation to production faster because the ML workflows and pipelines you designed are reproducible end-to-end. Your monitoring catches issues before they ever reach a live AI agent in a hospital setting.
