Rosebud AI

Position ID: 2217-SWE [#13661]
Position Title: Machine Learning/Deep Learning Engineer
Position Type: Government or industry
Position Location: SAN FRANCISCO, California 94103, United States [map]
Subject Areas: Machine Learning, Deep Learning, Neural Networks, Software Engineering
Application Deadline: 2020/02/29 (accepting applications posted 2019/02/20, listed until 2020/02/29)
Position Description:    

Overview:

Rosebud is a startup pioneering virtual try-on in the beauty and retail markets.

Rosebud Description:

The beauty industry is a half a trillion dollar market with high margins. With Instagram and other visually based social channels moving product discovery from brick and mortar to mobile and web, building tools to improve direct to consumer engagement provides a huge competitive advantage. Advancements in deep learning for computer vision and image synthesis can be leveraged for better curation of products and content and can also be used for high touch engagement via virtual try on. Current virtual try on technology uses classic computer vision to detect features (eg. eyes, mouth) and uses an AR layer of color and texture to create the illusion of trying on specific products. This process is laborious and requires manual calibration for different skus. The vision of Rosebud AI is to use recent advancements in image synthesis using GANS and other generative models for this task. Building tech to improve virtual try on can also serve as a backbone for revolutionizing general retail from brick and mortar to online. Rosebud’s vision is to supercharge an ecommerce native, consumer focused, data driven beauty distribution channel.

Team: Rosebud was founded by Lisha Li and funded by top Silicon Valley VCs. Angel investors include Rachel Weiss, VP of digital innovation at L’oreal, Jake Klamka (founder CEO of Insight Data) and Dmitry Kisluk (Visual Search and ML lead at Pinterest). Prior to Rosebud, she was a principal at Amplify Partners, a 200 million dollar venture capital firm based in Silicon Valley focused on deep tech and artificial intelligence. At Amplify she invested in and helped seed and series A startups leverage machine learning, computer vision and generative models, touching on areas such as the creative process, machine learning infrastructure, compute substrates and manufacturing.  Her investments include Covariant.ai, Glia and Strangeworks, and she was a board observer at Covariant and Primer.ai.  Lisha completed her PhD at UC Berkeley in deep learning and mathematics, she developed novel deep learning architectures for graphs (graph convolutions). She also worked on the data science teams at Pinterest and Stitch Fix.  Advisors of Rosebud include top researchers in image synthesis using GANs.

Positions: Please apply by emailing. Decisions are made on a rolling basis, so applying sooner will help your chances.

Machine Learning Engineering at Rosebud:

In this role, you’ll build and implement novel Machine Learning and Deep Learning systems, as well as helping to build the infrastructure to train and deploy them. Specifically, you’ll: Design and implement the infrastructure required to train and run inference of models at scale. Build state-of-the-art machine learning and deep learning model Work with the mobile development team to build real-time systems for model serving Work with the data team’s infrastructure to build real-time and offline feature databases As we grow, scale the ML system to be able to support more use cases and ML model types Qualifications & Requirements for ML Engineer Nice to have: Experience integrating with front end mobile systems, like React Native. Experience working with Google App Engine. Experience re-implementing deep learning models from papers. Especially previous work with GANs (and cycleGANs). One of the following: (a) BS or MS in CS or related field with 1+ years of experience in implementing and deploying large scale ML solutions (but honestly high school drop out is fine if you are a amazing dev). OR (b) Ph.D. in Machine Learning, Statistics, Optimization, Physics, or related field, with 1+ years experience building production-ready ML models and systems Strong software engineering fundamentals - understanding of data structures and algorithms, O-notation, ability to maintain a test suite and write clear maintainable code Familiarity with a majority of the following tools: Pytorch, Tensorflow, Numpy, Scipy, pandas, scikit-learn, Google App Engine. Strong programming skills in Python and ability to wrangle data from many disparate data sources Professional experience in either mobile development or full stack engineering Technologies we use: Pytorch, React Native, Google App Engine.

Salary: Competitive

Mobile Developer at Rosebud:

In this role, you’ll build the mobile app for Rosebud AI. Specifically, you’ll: Use React Native to implement the mobile app.

Nice to have: previous experience working with consumer applications, especially ones doing deep learning inference on the backend is a big plus. Qualifications & Requirements for Mobile Dev One of the following: (a) BS or MS in CS or related field with 1+ years of experience in implementing and deploying large scale ML solutions OR (but honestly high school drop out is fine if you are a amazing dev). OR (b) Ph.D. in Machine Learning, Statistics, Optimization, Physics, or related field, with 1+ years experience building production-ready ML models and systems Strong software engineering fundamentals. Familiarity with a majority of the following tools: React Native. Google App Engine. IOS native.

Salary: Competitive.


This employer is not accepting applications for this position through Mathjobs.Org. Please see the job description above on how to apply.
Contact: Xiang, 5109907869
Email:
Postal Mail:
500 3rd St, San Francisco, CA 94107
Web Page: www.rosebud.ai

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