Machine Learning Engineer at Scale
San Francisco, California, United States
🇺🇸 (Posted Jan 23 2019)
About the company
Scale is a rapidly growing post-Series B startup. Our mission is to accelerate the development of AI applications. 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, and we've become an industry standard for the self-driving car market.
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.
Create optimized and efficient tooling, like Guided Automatic Segmentation, for taskers to complete complex tasks with speed and accuracy.
Reliably evaluate data quality at scale.
Intelligently route tasks from customers to specialized taskers for low turnaround and high accuracy.
Automatically hire, train and onboard taskers.
Skills & requirements
This role could be a fit if you have experience in one of the following:
Deep Learning: building CNNs.
Classical Machine Learning: non-deep learning methods (random forests, collaborative filtering, HMMs, etc.)
Applied ML Engineering: building large-scale data and machine-learning pipelines.
Experience with TensorFlow and/or Pytorch.
At least a Bachelor’s degree (or equivalent) in a relevant field.