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ML Jobs Newsletter - Issue #15

ML Jobs Newsletter - Issue #15
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Since our last edition of the ML Jobs Newsletter, there have been a wave of data science career thought pieces:
Tim Hopper shared ‘Some Reflections on Being Turned Down for a lot of Data Science Jobs’ while Vicki Boykis‘ blog post 'Data Science is different now’ describes the divide between the skills emphasized in data science MOOCs/job descriptions and those in actual data science work loads.
One skill Vicki recommends learning definitely stood out to us here at
5) Making models and data reproducible 👈🏼👈🏼
Interested in meeting and learning from data scientists? For subscribers in New York, we’d like to invite you to our next meetup on productionizing machine learning models on March 7th at GroupM (see the link below).

Talk Night @ GroupM: ML Models in Production, co-hosted w/ NYC Python | Meetup
Curated with love by
Project Visualizations in - comparing model iterations
Project Visualizations in - comparing model iterations
Comet is doing for ML what GitHub did for code. We allow data science teams to automatically track their datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility.
Want your job listing to be featured?
This bimonthly newsletter gives you access to the latest machine learning roles across the world.
If your company is hiring for awesome data scientists, machine learning engineers, or research scientists and would like to be included here, send an email to me at
Featured ML Job:
Machine Learning Engineer - Rosebud
> email with cv for application
What’s hot about this job?
Rosebud is a startup pioneering virtual try-on in the beauty and retail markets. 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. 
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.
ML Jobs:
Machine Learning Leadership Roles:
Mid-Career ML Roles:
Early-Career ML Roles:
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