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About Clipbook Clipbook’s mission: reinvent how leaders listen to and engage with the world of information that comes out every day. Our platform ingests, comprehends, summarizes, and surfaces insights and actions from traditional news, TV, podcast, social media, and internet data at scale—across text, audio, and video in real time. We launched in 2023 and have grown to 200+ clients including BCG, Weber Shandwick, and dozens of government agencies. We bootstrapped to seven-figures in ARR before raising a $3.3M seed round (co-led by Mark Cuban and a host of prominent angels and VCs). Our founding team has backgrounds at BCG, Bain, Harvard, Stanford, Oxford, the White House, and Congress, and has previously built startups backed by Sequoia, Tiger Global, Insight Partners, Coatue, and NFX. The Role You'd be a key contributor at Clipbook — joining a small but mighty engineering team with engineers from Meta, Stripe, and AWS. You'll have the opportunity to develop new features and capabilities, and what you build will ship to 200+ customers quickly. What You'll Do Architect & build core systems. Drive key decisions across our stack and own feature development end-to-end. Integrate AI into real-world workflows. Put LLMs and ML models into production for real workflows: RAG pipelines, embeddings, prompt execution, agentic systems, fine-tuned models. Design systems that scale. Build systems that will hold up as we grow 10x, while being practical about what to invest in now vs. what can wait. 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. What We're Looking For 3+ years building and scaling production 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. Genuine excitement to build quickly & ship fast. We care about getting things into users' hands and iterating from there. Experience with cloud infrastructure, containerization, and CI/CD. Familiarity with AWS/GCP, containerization (Docker/K8s), and CI/CD pipelines. Excellent communication skills Technical Areas Where Depth Matters (one or more) 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. Originally posted on Himalayas