Clipbook's mission is to reinvent how leaders listen to and engage the world by providing AI-powered solutions for the communications, public relations, and public affairs industries. The company is experiencing rapid growth and needs to scale its backend systems and data infrastructure to support a growing client base and complex AI integrations.
Requirements
- 3–5+ years building and scaling production backend systems.
- Strong across backend fundamentals (services, data models, distributed systems), with deeper expertise in one or more areas — and a desire to keep expanding your breadth over time.
- Strong experience with cloud infrastructure, containerization, and CI/CD.
- Familiarity with AWS/GCP, containerization (Docker/K8s), and CI/CD pipelines ensures we can deliver high uptime and iterate quickly.
- Experience architecting and optimizing data pipelines & storage systems.
- Ability to build scalable, cost-efficient data architectures and tune performance for large and variable workloads across text, media, and streaming sources.
- LLM integration and MLOps — fine-tuning, embeddings/RAG pipelines, or RLHF workflows
Responsibilities
- Architect & build core backend systems. Drive architectural decisions across our backend stack (Python, Node.js, PostgreSQL).
- Own features from concept → deployment → observing users rely on what you built.
- Design scalable data infrastructure. Build and maintain pipelines for ingesting, normalizing, indexing, and querying massive, multi-modal datasets across news, social media, policy, and more.
- Develop performant APIs and services. Create robust interfaces and internal services that power our product end-to-end, ensuring reliability, security, and a seamless experience for customers.
- Integrate AI into real-world workflows. Productionize LLMs and classical ML models for high-uptime applications — including prompt execution, embeddings, RAG pipelines, and more.
- Shape Clipbook’s engineering culture. As an early engineer, you’ll influence everything — code quality, system design principles, documentation standards, and how we build as a team.
- Solve the hardest problems in large-scale data engineering, semantic search, and AI-powered analytics across all media modalities — e.g., how do we process and index a podcast in a way that captures its meaning, not just the transcript?
Other
- Genuine excitement to build quickly & ship fast.
- Strong product sense and a bias for action.
- A future leader. You’ll help shape our engineering culture and can quickly grow into leadership as the company scales.
- We care more about what you ship than when/where you work — but we do believe spending time together in-person (we’re hybrid, in the office 2–3x/week) meaningfully accelerates product velocity and team chemistry.
- We believe leaders stay hands-on: even as we grow, everyone (including managers) continues to ship.
