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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 from traditional news, TV, podcasts, social media, and internet data across text, audio, and video in real time. 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. 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.
As a Founding Senior Software Engineer, you'll be a key contributor to a small but mighty engineering team alongside engineers from Meta, Stripe, and AWS. You'll have significant autonomy and impact, with the opportunity to develop new features and capabilities that ship to 200+ customers quickly.
Key responsibilities:
- Architect and build core systems, driving key decisions across the stack and owning feature development end-to-end
- Integrate AI into real-world workflows, putting LLMs and ML models into production for RAG pipelines, embeddings, prompt execution, agentic systems, and fine-tuned models
- Design systems that scale as the company grows 10x while being practical about investment priorities
- Develop 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 with strong backend fundamentals (services, data models, distributed systems) and deeper expertise in one or more areas
- Genuine excitement to build quickly and ship fast, with a focus on getting features into users' hands 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 technical areas: 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); or Backend (Python, TypeScript, PostgreSQL, high-concurrency systems)
