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 Engineering Internship program. You have the option of working from our San Francisco, Berlin 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 as one of the larger users of cloud resources for ML.

We are developing a variety of ML infrastructure tools to make our production services more efficient and our ML team more productive. We have a strong commitment to open source, and you may well find yourself working on projects that are released to the world.

Please send your CV, github, and a brief description of what most interests you, along with your dates of availability and preferred location.

IMI does not discriminate on the basis of race, creed, color, ethnicity, national origin, religion, sex, sexual orientation, gender expression, age, height, weight, physical or mental ability, veteran status, military obligations, or marital status.

Functional skills:

Some combination of the following:

• A basic knowledge of and interest in ML. Professional or Open Source Python experience.
• We would like to see an existing portfolio of work or references that can speak to your programming skills.
• Advanced Linux and Unix skills are also very important as you will be bootstrapping systems.
• We make heavy use of Kubernetes and Docker containers, but it is not a prerequisite.
• We do continuous integration and continuous delivery, so testing is very important to us.
• Please be comfortable writing unit, functional, and integration tests for your work.

Depending on your skills and preferences, you may work with some of our research scientists to take their work into production or improve the infrastructure used for training and inference. We work primarily in Python, with pyTorch and Tensorflow being our preferred tools for research.

Machine learning and AI products and services