SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: USD 200,000 - 350,000 / annual
Known is an AI-native dating app that uses conversational AI to help users find meaningful connections. Rather than swiping, users share their life story through a 27-minute voice conversation with an AI matchmaker, which builds a deep personal profile to power intelligent matching.
You'll join a team of ex-founders and experienced engineers from Uber, Uber Eats, Airbnb, Bland, and Faire to build the backend infrastructure powering Known's core product. This is a unique opportunity to work at the intersection of large-scale consumer applications and frontier AI.
The role offers significant autonomy and ownership. You'll decide which part of the backend stack excites you most and own systems end-to-end. Potential focus areas include:
- Designing and scaling backend services, APIs, and infrastructure for the consumer experience
- Building real-time systems supporting conversational AI, voice interactions, and agentic workflows
- Architecting data pipelines and storage systems for matchmaking, personalization, and machine learning
- Developing reliable, low-latency services connecting AI agents to real-world actions and third-party integrations
- Improving performance, reliability, security, and scalability as the platform scales to millions of users
The team works in-person in Cow Hollow, San Francisco, with a culture emphasizing high autonomy, ownership, and shipping velocity.
REQUIREMENTS:
- 4–6 years of experience designing, building, and deploying production backend systems
- Strong proficiency in at least one backend language (Python, Go, TypeScript, Java)
- Experience designing APIs, distributed systems, and scalable service architectures
- Experience with relational databases, caching, asynchronous processing, and data infrastructure
- Experience with cloud infrastructure and deployment (Docker, Kubernetes, AWS/GCP)
- Strong product intuition and bias toward shipping, iterating, and taking ownership
