Introduction

As a Machine Learning Engineer Intern, you will collaborate with a small, agile team of brilliant and curious people. You will play a key part in enhancing our Machine Learning technology ecosystem by working on a well-defined deep learning project to improve the confidence and calibration of our production ML models. You will get to put your stamp on an evolving ML roadmap that will increase value for our Enterprise level customers and their internal users. Does hands-on engineering in an innovative environment excite you? If so, then apply today! We’d love to meet you.

We are looking to hire an intern for the summer of 2022. The student will primarily work on

production NLP – namely, helping us improve our model confidence in production. 

Responsibilities

• Timeline: approx. 2 months
• The intern will work on the Production Model Monitoring project. They will primarily research ways to make our models more confident in production. This will involve:
• Training our current NLP models with an energy-bounded objective function (approx. 2-3 weeks).
• Start with training on one of the initial tasks, such as sentiment analysis, then extend to our greater multitask model. Don’t need to worry about integration
• Validating the model and verifying that it is more confident (approx. 2-4 weeks)
• Can use statistical techniques to gauge difference in the distributions
• Can use unsupervised techniques to model the distributions
• Construct a report with relevant results and next steps (approx. 2 weeks)
• Comp range: 35-45 per hour
• 11.2k – 14.4k for summer (40 hrs/week, 8 weeks)

Requirements

• Experience with programming and the common Python numerical computing frameworks (NumPy, Pandas, Scikit-learn, PyTorch, etc.)
• Ideally a STEM degree (CS, Math, Physics), but can be waived for those w/ project experience and interest in the field.
• Familiarity and interest in NLP / deep learning.
• Solid mathematical foundations and ML basics.
• Good communication skills.
• Bonus if the candidate has project experience with DevOps tools (Docker, Kubernetes) and full stack technologies (Django, Flask)