As a Machine Learning Research Intern, you will:
The goal of the ML team at Scale is to develop machine learning solutions advancing the company mission. Our current focus areas are Computer Vision ( 2D/3D detection, 2D/3D segmentation, object tracking), Machine Learning (e.g. semi-supervised learning, active learning) and Natural Language Processing.
We are building a large hybrid human-machine system in service of ML pipelines for dozens of industry-leading customers. We currently complete millions of tasks a month, and will grow to complete billions of tasks monthly.
- Research and develop machine learning solutions to assist humans in the loop.
- Aid in the creation of high quality ground truth data with speed and accuracy.
- Take state of the art models developed internally and from the community, use them in production to solve problems for our customers and taskers.
- Take models currently in production, identify areas for improvement, improve them using retraining and hyperparemeter searches, then deploy without regressing on core model characteristics
- Work with product and research teams to identify opportunities for improvement in our current product line and for enabling upcoming product lines
- Work with massive datasets to develop both generic models as well as fine tune models for specific products
- Currently enrolled in a PhD Program with a focus on Machine Learning, Deep Learning, Computer Vision or Natural Language Processing.
- Have had a previous internship around Machine Learning, Deep Learning, Computer Vision or Natural Language Processing.,
At Scale, our mission is to accelerate the development of Machine Learning and AI applications across multiple markets. Our first product is a suite of APIs that allow AI teams to generate high-quality ground truth data. Our customers include Alphabet (Google), Zoox, Lyft, Pinterest, Airbnb, nuTonomy, and many more
Scale is an equal opportunity employer. We aim for every person at Scale to feel like they matter, belong, and can be their authentic selves so they can do their best work. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.