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Clipbook is reinventing how leaders listen to and engage with information at scale. The platform ingests, comprehends, summarizes, and surfaces insights and actions from traditional news, TV, podcasts, social media, and internet data in real time across text, audio, and video. Founded in 2023, Clipbook has grown to 200+ clients including BCG, Weber Shandwick, and dozens of government agencies. The company bootstrapped to seven-figures in ARR before raising a $3.3M seed round co-led by Mark Cuban and prominent angels and VCs. The founding team includes leaders from BCG, Bain, Harvard, Stanford, Oxford, the White House, and Congress, with prior startup experience backed by top-tier VCs.
You will join a small but mighty engineering team with engineers from Meta, Stripe, and AWS as a key contributor. You'll architect and build core systems, driving key decisions across the stack and owning feature development end-to-end. Your work will ship to 200+ customers quickly.
Key responsibilities include:
- Architecting and building core systems, driving key technical decisions and owning features end-to-end
- Integrating AI into real-world workflows: deploying LLMs and ML models in production, building RAG pipelines, managing embeddings, executing prompts, developing agentic systems, and fine-tuning models
- Designing systems that scale as the company grows 10x while being pragmatic about current vs. future investments
- Developing performant APIs and services that power the product end-to-end with reliability, security, and seamless customer experience
REQUIREMENTS:
- 3+ years building and scaling production systems
- Strong backend fundamentals (services, data models, distributed systems) with deeper expertise in one or more areas and desire to expand breadth
- Genuine excitement to build quickly and ship fast, with focus on getting features to users and iterating
- Experience with cloud infrastructure, containerization, and CI/CD (AWS/GCP, Docker/Kubernetes, CI/CD pipelines)
- Excellent communication skills
- Depth in one or more of: Data Engineering (Spark, Kafka, Flink, BigQuery, streaming pipelines, ETL at scale); Distributed Systems (high-volume scaling, fault tolerance, eventual consistency); AI/ML (LLMs in production, RAG, embeddings, fine-tuning, inference optimization); Backend (Python, TypeScript, PostgreSQL, high-concurrency systems)
