Machine Learning Research Fellows

Intuition Machines

machine learning python research backend big data machine learning python ai big data machine learning

Posted 5 months ago

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2019 ML Research Fellows – Job Description

Interested in working on hard problems at scale?

IMI builds ML products and services used by some of the largest companies in the world. Come learn from our veteran team of machine learning and distributed systems experts, hailing from Stanford, MIT, Apple, and Cloudera.

We are now opening applications for our 2019 Fellows program. You have the option of working from our San Francisco or Helsinki offices, or remotely: we have a large distributed workforce supplementing the teams in our offices, and are comfortable with remote collaboration.

As a discipline still finding its theoretical footing, doing ML at scale tends to uncover unique problems and occasionally spark unique insights. Our work is geared towards applying some of our more theoretical ideas, as we believe this is one of the best ways to push forward the field.

We conduct original research in areas like unsupervised and active learning, motivated by the unique problems and datasets we have access to as one of the larger users of cloud resources for ML.

You will have the opportunity to publish with our scientists if your work yields results, and we have a strong commitment to open source code and (when possible) datasets.

Research interests:

We are very interested in taking a more geometric view of neural network processes. A background in algebraic geometry, representation theory, or statistical physics will be helpful.

Separately, we are investigating future directions for active and online learning, with the unique capacity to rapidly test and apply at scale ideas in this area.

Finally, we are pursuing some interesting directions in multi-task or joint representation learning for anomaly detection, and in time series analysis.

Functional skills:

We work primarily in Python for research, with pyTorch and Tensorflow being our preferred tools. You will have access to our in-house distributed training and inference infrastructure, which has been designed for ease of use but still benefits from basic knowledge of distributed systems.

We also strive for a high degree of programming competence within our research group, as we have found that good discipline in implementing ideas makes your task easier and collaboration more pleasant. If you are one of the many excellent researchers who have never written a unit test this will likely change.

Please send your CV, github, and a brief description of what most interests you, along with your dates of availability and preferred location to <[email protected]>.

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