Software engineering

Deep Learning Engineer

  • Location
    Remote / Relocation
  • Type
    Full-time candidate
  • Salary
    $85k - $155k / year
  • Budget
No items found.
About the role

Build the future of content creation with us. You get to work on implementing bleeding-edge research for generating videos and images and scale them to production.

We support a growth mindset, perks include conference travel and paper publications and mentorship from top researchers in the field.

This is a remote position with an opportunity for relocation to SF in the future.

Working hours should overlap at least 4 hours with PST (6 pm - 10 pm Kyiv time).

Responsibilities and what we are looking for:

  • Training and deploying large-scale Deep Learning models on cloud infrastructure. We use AWS and GCP.
  • Familiarity with GANs (eg. StyleGAN, conditional GANs, image2image translations, vid2vid), Autoregressive models, Flow models is a huge plus. We use Tensorflow and Pytorch.
  • You are excellent at debugging deep learning models and are an expert at implementing models from scratch, using cloud infrastructure to train and deploy these models.

Interview process

We are not going to hold a whiteboard coding interview - we are much more excited to look at the things that they've worked on, so please include a link to your GitHub/GitLab or product/project pages, resembling your specific work experience with GANs.

About you

  • You are a strong individual contributor, with a low ego and high intellectual curiosity;
  • You are excited about implementing your own ideas and making them work;
  • You don’t need much direction and keep documentation well-organized.

Company culture

  • We are a tiny team with complementary skill sets, driven by being on top of the bleeding-edge technology.
  • We value independence and proactivity.
  • We are sticking to an ‘open office’ concept, so every member of the team can see where you’re at and vise versa.

Note: If you have a lot of computer vision experience, but not in particular with GANs, you should have implemented papers yourself, especially in GAN-type architectures (e.g. StyleGAN/ CycleGAN), and have done end-to-end deployments there. We have a lot of features we want to implement and they're pretty finicky to train so we're really prioritizing the people who have worked with these architectures, debugged them, and scaled up experimentation.

About the company

We use generative adversarial networks (GANs) and other bleeding-edge techniques from deep learning to synthesize photos and videos of people. Creative and marketing teams use Rosebud to replace models and photoshoots. We already have 4000 people using our AI synthesized stock photos that offer a diverse array of models.

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.

We are backed by YCombinator, Khosla Ventures, Amplify Partners and Lux Capital. With participation from 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 app.generative.photos.

We are a tiny team, fully distributed with a San Francisco HQ, led by CEO Lisha Li, and are looking for a Deep Learning Engineer to join our team.

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