ResiQuant is addressing the lack of accurate, standardized, and dynamic property data for disaster resilience, which leads to decisions about risk being made with incomplete or incorrect information. They aim to empower insurers, financial institutions, and asset managers with AI systems fused with structural engineering expertise to bridge critical data gaps and deliver precise, actionable building insights for better decision-making.
Requirements
- Experience shipping AI-assisted or data-intensive services (e.g., integrating model APIs or retrieval-backed features) and measuring their quality and performance.
- AI tool proficiency - experienced using modern AI tools for automation, prototyping, and productivity
- Python, AWS/GCP, CI/CD pipelines.
- Design/evolve PostgreSQL schemas
- Add and maintain observability (logs/metrics/traces), dashboards, alerts, and service SLOs.
- Improve performance & reliability (query tuning, indexing, caching/Redis, connection pooling; retries/backoff, DLQs, replay/backfills)
- Evolve CI/CD (GitHub Actions)
Responsibilities
- Co-design and build scalable backend services supporting a growing base of enterprise customers.
- Build and optimize AI-assisted backend capabilities from API design through production deployment.
- Create maintainable interfaces to evaluate and improve models and retrieval-based features, with strong monitoring for quality, latency, and reliability.
- Develop robust pipelines that ingest, validate, and transform large property and risk datasets for downstream applications.
- Add comprehensive observability, safety checks, and performance improvements to deliver enterprise-grade applications.
- Design/evolve PostgreSQL schemas; write safe migrations with rollout/rollback plans.
- Build backend APIs for AI-powered features
Other
- Own entire backend features from conception to production deployment
- Build in a fast-paced AI company handling enterprise-scale data
- Architect customer integrations for large insurance companies
- Work directly with founders on product strategy and technical decisions
- Full commitment to in-person work - willing to be on-site in San Francisco 5 days/week for rapid collaboration with the team and work long hours.
