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Pear VC

Backend Engineer - Known

San FranciscofulltimemidAdded 1 month ago

About this role

As a Backend Engineer at Known, you will play a pivotal role in developing the backend systems and infrastructure that support the platform, such as APIs and real-time communication pipelines. This position demands collaboration with the founding team to create reliable and scalable services, making foundational infrastructure decisions for growth.

What you'll do

  • Design and build scalable backend services using Node.js/TypeScript
  • Architect and optimize database systems with Postgres
  • Implement real-time communication infrastructure using WebSockets and WebRTC
  • Integrate third-party services with robust monitoring
  • Collaborate with AI/ML engineers on data pipelines and APIs
  • Own deployment and observability, ensuring system reliability

What they're looking for

  • Proficient in Node.js and TypeScript
  • Expert-level knowledge of Postgres
  • Experience with RESTful API design
  • Hands-on with cloud platforms (AWS, GCP)
  • Familiarity with Docker and Kubernetes
  • Knowledge of WebSockets and WebRTC
  • Strong grasp of observability and monitoring
  • Focus on security and data privacy
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Pear VC

Pear VC invests in and supports AI-driven enterprise software companies building automation platforms, compliance systems, and specialized tools across industries like financial services, insurance, and software development. The portfolio companies are hiring founding and senior engineers to design core infrastructure, build AI systems, and develop scalable backend platforms alongside their leadership teams.

View all jobs at Pear VC

Likely interview questions

  • Walk us through how you'd design a high-throughput API layer that serves personalized recommendations with sub-100ms latency. What caching and database optimization strategies would you use?
  • Tell us about your experience building and scaling WebSocket infrastructure for real-time features. How have you handled connection management, broadcasting, and reliability at scale?