AI/ML Job: Machine Learner

UnifyID

Machine Learner at UnifyID

San Francisco, California, United States 🇺🇸   (Posted Jun 7 2018)
About the company
Few startups have the luxury of working with a technically strong team on something both interesting and impactful. At UnifyID, we thrive on tackling the hard stuff first, working closely with each other in building meaningful products to make how we see authentication today vastly different from the status quo: passwords. Imagine a world where you are seamlessly authenticated for purchases, can securely access your smart devices pin-free, and never needing to provide your mother’s maiden name as a way to show that it is really you.

UnifyID is in the enviable position of working on a product with white-hot demand from enterprise clients across the globe seeking easier and more secure ways to authenticate their end users.

Contribute to the bleeding edge of machine learning, security, and implicit authentication at an early-stage VC funded startup in the heart of SoMa, San Francisco. Come join our team and radically change authentication forever.

Job position
Permanent

Job description
About the Role

We are looking for a math+code engineer/signal-processor/hacker/self-proclaimed individual who is comfortable with crafting, hacking, implementing, re-implementing and most importantly, breaking Machine Learning algorithms deep, shallow, or otherwise.

Join Us!

We are seeking passionate, self-motivated individuals who are ready to get fully immersed into the details of crafting the future of authentication. We encourage and support everyone on the UnifyID team to collaborate, publish, teach, and learn. UnifyID is located in the heart of SoMa in San Francisco and has unparalleled access to deep entrepreneurial expertise, high-caliber academic research institutions, and top-tier VC resources. Finally, it is important that you be authentic and be yourself. UnifyID is an equal opportunity employer.

Skills & requirements
If you think you have the answers/(mis-)informed opinions on one or more of the following questions, we’d like to hear from you! (a.k.a. you’ll love it here!)

The great generative model wars [GANs vs Pixel-RNNs vs VAEs]: Who do you think will win and why?

Word on the Kaggle street is that XGBoost is killing it! Why do you think this is?

Do you think that the problem of counting independent sets in a bipartite graph is not #P-complete but #BIS? Why so?

As language/platform agnostic as we are (we use Lua, Python, Julia and R on a daily basis and are eagerly awaiting the Milk compiler from the CSAIL folks too), we expect you to be unreasonably good at and evangelize at least one of the tuples in the Cartesian product of L X P X O where L={Python, Scala, Julia, R, Lua, C++, Java}, P={Scikit-learn, Torch/Autograd, Caffe, Keras with Theano/TensorFlow, Chainer}, and O={Ubuntu, OS X, RHEL / CentOS / Fedora} and pick things up when required.

Instructions how to apply
see the website
[ job website ]

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