Infra / Platform Engineer - Known

Pear VC • Austin, TX

77d

About The Position

You’ll be the foundational engineer owning Known’s 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, you’ll design and scale the systems that make Known fast, reliable, and secure. You’ll work directly with the founding team (AI/ML, product, and design) to establish Known’s 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.

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.
• DevOps fundamentals: observability, cost optimization, and security.
• Collaborative mindset, strong ownership, and bias toward shipping working systems fast.

Nice To Haves

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

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 11-50 employees