Machine Learning Engineer

San Francisco, California
About Scale
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.
As a machine learning engineer, you will:
  • 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.
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.
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.