About the Role

Youll be the foundational engineer owning Knowns core infrastructure and platform systems the backbone that powers our AI-driven matching voice and scheduling experiences. From cloud infrastructure and data orchestration to performance monitoring and model deployment assistance youll design and scale the systems that make Known fast reliable and secure.

Youll work directly with the founding team (AI/ML product and design) to establish Knowns technical foundation shaping not just our architecture but our engineering culture and best practices from day one. This role is ideal for a pragmatic builder who enjoys going from blank slate to production and thrives in early-stage environments where reliability velocity and simplicity matter most.

Responsibilities

• Design and manage cloud infrastructure (AWS-first with IaC via Terraform).

• Establish CI/CD pipelines and best practices for rapid safe iteration (GitHub Actions Docker Kubernetes etc.).

• Build and maintain scalable data ingestion and orchestration pipelines to support ML and product analytics.

• Administer and optimize our databases PostgreSQL (with pgvector for embeddings) and analytical warehouse.

• Collaborate with AI/ML engineers to deploy and monitor LLM and matching models for inference evaluation and retraining.

• Implement observability (logging metrics traces alerts) across backend services data jobs and model endpoints.

• Drive reliability and scalability across our web mobile and agentic systems from real-time voice matching to background batch workflows.

• Collaborate cross-functionally with product design and ML teams to ensure infrastructure aligns with user and business needs.

Requirements

• 4 years of experience in infrastructure platform or data engineering (startup or high-growth environments preferred).

• Strong proficiency in Python TypeScript and scripting (Bash/YAML).

• Deep understanding of cloud architecture (AWS GCP or similar) and Infrastructure-as-Code (Terraform Pulumi or CloudFormation).

• Solid experience with containerization and orchestration (Docker Kubernetes ECS).

• Proven ability to design and operate data pipelines and distributed systems.

• Experience with PostgreSQL (ideally with pgvector or embeddings) data modeling and schema design for real-time and analytical workloads.

• Familiarity with ML/AI workflows (model training inference monitoring) and feature stores is a plus.

• DevOps fundamentals: observability cost optimization and security.

• Collaborative mindset strong ownership and bias toward shipping working systems fast.

Example Projects

• Stand up a data lake warehouse for storing and analyzing user signals transcripts and model outputs.

• Build real-time ingestion from app agent and third-party APIs (OpenAI Twilio Stripe).

• Deploy and scale voice agent infrastructure with low-latency streaming recording and monitoring.

• Design the CI/CD and observability stack for Knowns core services.

• Assist ML engineers in Implementing model deployment pipelines for embedding generation and re-ranking inference.

Why This Role

Youll help define the technical foundation of a product that blends human connection and advanced AI. As one of Knowns first engineers youll make decisions that influence how the product scales performs and evolves from the data stack to the deployment layer.
If youre excited by the idea of shaping the platform behind a category-defining AI product this is the place to build it.