Sage is looking to solve the problem of archaic and ineffective fall prevention and emergency response systems for older adults in senior living facilities by building a modern AI-powered care intelligence platform.
Requirements
- 3+ years of professional backend development experience
- Strong proficiency in Kotlin, Java, or similar JVM languages
- Experience building and maintaining distributed systems and RESTful APIs
- Demonstrated ability to debug complex production issues and optimize system performance
- Experience with cloud platforms (AWS or GCP) and containerized deployments
- Experience with video processing, streaming protocols (RTSP/RTMP), or FFmpeg
- Hands-on experience with prompt engineering, multi-modal foundational models, and LLM/VLM optimization techniques
Responsibilities
- Design, build, and optimize real-time video processing pipelines that handle hundreds of concurrent streams, implementing fault tolerance and auto-recovery mechanisms
- Debug and resolve complex distributed systems challenges including VPN connectivity, memory optimization, concurrency issues, and stream processing failures across multiple facilities
- Improve system scalability and stability by architecting service separations, implementing proper monitoring and alerting, and ensuring 99.9% uptime
- Experiment with AI model integration, including prompt engineering for multi-modal models and implementing cost-optimization strategies like intelligent gating and caching
- Develop features and tools that surface actionable insights about resident activities, helping caregivers provide more timely and effective care
- Architect privacy-preserving video storage and replay systems, including de-identification pipelines for building long-term datasets
- Work cross-functionally with product and engineering teams to expand the platform's capabilities from acute event detection to long-term health insights
Other
- The role offers a unique blend: architecting scalable infrastructure, solving real-time processing challenges, optimizing system performance, while also experimenting with multi-modal AI models and crafting prompts that improve detection accuracy.
- Our immediate focus is life-saving fall detection, but we're building the foundation for comprehensive care insights - creating a platform that will transform how senior care communities understand and respond to resident needs.
- We believe in cross team collaboration. We think good ideas can come from anyone, and we've designed our processes to encourage participation from all.
- While we take our mission seriously, we don't take ourselves too seriously. We like to host offsites, outings, and team meals where we can connect as people, not just as colleagues.
- While we are an in office culture, we allow up to 2 remote days per week.
