Clipbook builds AI-powered media monitoring and analytics tools for communications, PR, and public affairs teams. We help them track, search, and make sense of coverage across text, audio, and video.
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). We plan to raise our Series A this year and we are aiming for eight-figures revenue by end of year.
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 RoleYou'd be one of the first engineers at Clipbook — joining a small but mighty engineering team with engineers from Meta, Stripe, and AWS. You'll have real influence over architecture and technical direction, and what you build will ship to 200+ customers quickly.
What You'll DoArchitect & 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. We're still laying critical foundations, so we value a pragmatic, "strong opinions, weakly held" mindset — especially when decisions are expensive to unwind later.
Design scalable data infrastructure. Build and maintain pipelines for ingesting, normalizing, deduplicating, indexing, and querying massive, multi-modal datasets (text, audio, video) across news, social media, policy, and more. A lot of this is normalization, deduplication, and edge case handling.
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. Production means reliable, monitored, and cost-conscious.
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.
Collaborate closely with users. Join customer calls, hear what's working and what isn't, and build in response. Rapidly iterate to deliver solutions that genuinely move the needle for comms/public affairs teams.
Shape Clipbook's engineering culture. As one of the first engineers, you'll influence everything — code quality, system design principles, documentation standards, and how we build as a team. We believe leaders stay hands-on: even as we grow, everyone (including managers) continues to ship.
2–10+ years building and scaling production backend systems. You've taken systems from zero to production and owned the full lifecycle. 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.
You take ownership. When something is broken or unclear, you fix it or flag it without waiting to be asked.
Comfortable with ambiguity. You make reasonable calls with incomplete information, communicate them clearly, and adjust as you learn more.
Strong experience with cloud infrastructure, containerization, and CI/CD. We're building systems that will one day serve Fortune 500 executives in real-time — so reliability matters. Familiarity with AWS/GCP, containerization (Docker/K8s), and CI/CD pipelines ensures we can deliver high uptime and iterate quickly.
A future leader. You'll help shape our engineering culture and can quickly grow into leadership as the company scales.
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, Node.js, PostgreSQL, high-concurrency systems.
Computer vision or multimodal model experience (audio, video, image). This is central to our product, so it's a meaningful differentiator.
Semantic search, vector databases, or meaning-aware retrieval.
LLM fine-tuning, RLHF, or eval pipelines.
Web scraping and API integrations at scale.
Startup founder or early-stage experience is a huge plus.
We're hiring for the application and data layer. This probably isn't the right fit if your background is primarily in firmware, embedded systems, hardware engineering, networking, or infrastructure/DevOps.
A Few Things Worth KnowingWe care more about what you ship than when or where you work, but this is an early-stage company growing fast. The pace reflects that. Our culture is intense and driven by H&H (hunger and hustle).
As one of the first engineers, you'll wear a lot of hats. There's no platform team to hand things off to yet.
Salary: $150K–$220K
Equity: Early-stage grant with significant upside
Benefits: Medical, dental, vision, 401(k), unlimited PTO
Growth: A clear path to engineering leadership as we scale
