About the job
Greetings from Deepnote! We empower data teams to tackle their toughest challenges. This mission goes beyond advanced algorithms, expansive datasets, and enhanced computing power; it necessitates the creation of innovative tools that are yet to be developed. We are here to pioneer those solutions.
As the premier data notebook platform globally, we are honored to serve clients such as Coca Cola, SoundCloud, NASA, Gusto, Webflow, Ramp, Harvard University, among many others. As we expand our dedicated team, we are on the lookout for kind, inquisitive, and talented individuals eager to grow alongside us and shape the future landscape of data science.
In this role, you will confront some of the most complex challenges in the field. While we don't expect you to possess expertise in every area, we do anticipate a rapid learning curve and a willingness to take initiative. You will be responsible for developing elegant and efficient code, gathering user insights, and crafting comprehensive workflows. You'll specialize in data science tools, enjoying the freedom to choose the technologies we adopt and the features we implement.
Why Join Us as a Software Engineer at Deepnote?
As a Deepnote Engineer, you will have end-to-end ownership of your projects, from conception through planning, delivery, and monitoring.
Complete ownership and trust are integral to our culture. Our engineers tackle the most challenging engineering problems directly, assisting data teams in overcoming significant obstacles.
Each day presents a new technical and product challenge that requires innovative solutions and a proactive mindset.
Our Tech Stack
Typescript, React, GraphQL
Node.js, PostgreSQL, Redis
Kubernetes, AWS
Proficiency with Cursor, Claude Code, or a similar parallelized agentic coding tool is essential for this role.
Your Responsibilities
Develop new features across the entire stack , including backend logic, APIs, and refined UI components.
Conceive and refine AI-driven features (chatbots, copilots, code generation, agents).
Work with prompts, evaluations, and context management to deliver dependable AI functionalities.
Enhance our real-time collaborative notebook experience.
Adopt a data-centric approach: structure, transform, and manage data flow throughout the product.
