About the job
About Clipbook
Clipbook creates innovative, AI-driven media monitoring and analytics solutions tailored for communications, PR, and public affairs teams. Our tools empower users to effectively track, search, and comprehend media coverage across text, audio, and video formats.
Since our inception in 2023, we've rapidly expanded our clientele to over 200, including prestigious organizations like BCG, Weber Shandwick, and numerous government agencies. We achieved seven-figure annual recurring revenue (ARR) through bootstrapping before successfully closing a $3.3M seed funding round led by Mark Cuban. Our sights are set on raising a Series A round this year, with an ambitious goal of reaching eight-figure revenue by year-end.
Our founding team boasts impressive backgrounds from institutions like BCG, Bain, Harvard, Stanford, Oxford, and includes experience in the White House and Congress. We have previously developed startups that received backing from notable investors such as Sequoia, Tiger Global, Insight Partners, Coatue, and NFX.
The Role
As a pioneering engineer at Clipbook, you will join a dynamic and skilled engineering team, comprised of professionals from industry giants such as Meta, Stripe, and AWS. Your contributions will significantly shape our architecture and technical direction, delivering impactful solutions to our growing customer base.
What You'll Do
Architect & build core backend systems. Lead architectural initiatives across our backend stack (Python, Node.js, PostgreSQL). Take ownership of features from ideation through deployment, ensuring user satisfaction with your creations. As we establish key foundations, we value a pragmatic approach with strong opinions that are flexible to change.
Design scalable data infrastructure. Develop and sustain pipelines for processing and managing vast, multi-modal datasets (text, audio, video) sourced from news, social media, and policy arenas, focusing on normalization, deduplication, and handling edge cases.
Integrate AI into real-world workflows. Deploy LLMs and ML models into operational settings, including RAG pipelines, embeddings, prompt execution, agentic systems, and fine-tuning models, ensuring reliability, monitoring, and cost efficiency.
Design systems that scale. Create robust systems capable of supporting a tenfold growth while making pragmatic investment decisions on current versus future needs.
Develop performant APIs and services. Build reliable interfaces and internal services that drive our product, guaranteeing a secure and seamless experience for users.
Collaborate closely with users. Participate in customer interactions to gather insights on performance, identify issues, and iterate on solutions.

