Rosebud AI (S19)

Easiest way to create videos with virtual talent via a self serve app.

Full Stack Engineer

$50 - $120

Remote / Remote
Job Type
6+ years
About Rosebud AI

We're democratizing the creation of visuals, so that brands big and small can tell their story compellingly. We believe all image and video creation will be done via generative methods in 5 years. We're building that future.

Currently marketing teams and creative agencies use Rosebud to make infinite variations of people in their ads so they can target by detailed demographics. We can take an image of a 65-year-old woman driving a convertible down the Amalfi Coast and programmatically modify it into a 25-year-old Chinese woman or a man going through his midlife crisis.

About the role

Skills: Node.js, React, React Native, TypeScript, Amazon Web Services (AWS)

Rosebud was founded by Berkeley PhD CEO and a team of AI experts to disrupt media creation for enterprises. For example, our product TokkingHeads allows existing or virtual characters to be animated with real human voice or text. We believe all image and video creation will be done via generative methods in 5 years. It will enable visual storytelling at the speed of thought, a decentralized Hollywood on your laptop. We're building that future.

We are backed by Y Combinator with our seed led by Khosla Ventures with participation from Balaji Srinivasan (Former CTO Coinbase and A16Z GP), Ilya Sutskever (cofounder/chief scientist Open AI), and Kevin Lin (cofounder COO Twitch).

We launched 25,000 AI generated photos last November, which became a finalist in the design category for Product Hunt’s 2019 Golden Kitty Awards. We’ve been covered by the Washington post and have over 4000 users sign up and tens of thousands of downloads of our generated images on

Why Join Us:

  • Build the future of content creation with us. You get to work on turning bleeding-edge research for generating videos and images into commercial products.
  • We support a growth mindset, perks include conference travel and paper publications and mentorship from top researchers in the field.
  • We are a fully distributed team with a San Francisco HQ. Open to candidates internationally.

Responsibilities and what we are looking for:

  • Build Great Products. We care deeply about what our customers want. We constantly iterate on our products and prototype new ideas. You must be very good at interpreting customer requests, translating them into great user experience, and implementing it in code.
  • Tackle Challenging Problems. A lot of our underlying technology is based on machine-learning algorithms. ML-algorithms are often not designed to run in real-time and require unique runtime environments. You must have experience with cloud-based service-oriented asynchronous systems. You must also understand how to bridge the gap between asynchronous algorithms and synchronous user interactions with web and mobile applications.
  • Prototype New Features. We meet our customers where they are. This means rapidly building prototypes end-to-end, including storage, business logic, and user experience.
  • Productize Exciting Research. Our team includes engineers developing new machine-learning algorithms. You should be able to understand the constraints and requirements of algorithms and participate in productizing them.

About You:

  • You are located anywhere.
  • You speak and write English.
  • In a conversation you can make sure that you understood the other party, and that the other party understood you.
  • You have a very good understanding of modern container-based micro-services architecture and experience building it from scratch.
  • You have experience building non-trivial single-page web-applications or mobile-applications from scratch.
  • You conceptually understand how deep-learning models work and how they are trained.

Our tech stack:

  • React.js with TypeScript.
  • Node.js with TypeScript running on Firebase Functions.
  • Firestore and Google Cloud Storage.
  • PyTorch and Tensorflow wrapped in Flask and running in a Kubernetes cluster.

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