Senior Machine Learning Engineer

San Francisco /
Engineering /
Full-time
About Labelbox

Labelbox is building infrastructure for data science teams to manage training data for neural networks. It's easy to take for granted the existence of collaborative tools for tasks like writing and debugging code; the machine learning world has no standard tooling for labeling data, storing it, debugging models and continually improving their accuracy. Enter Labelbox. Our vision is to become the go-to software platform for data scientists to collaboratively manage their data and train neural networks, all in a tight feedback loop.

Labelbox is experiencing massive growth, and we are looking to expand our engineering team to meet the demands of our burgeoning customer base which includes companies like American Family Insurance, Lytx, Airbus, Genius Sports, Keeptruckin and others. Labelbox is venture backed by Andreessen Horowitz, Gradient Ventures (Google’s AI-focused venture fund), Kleiner Perkins and First Round Capital and has been featured in Tech Crunch, Web Summit and Forbes.

Qualifications
• Masters or PhD in CS preferred or equivalent experience
• Expert in deep learning and computer vision
• Excellent developer with experience building production-scale data pipelines and web applications in Python 
• Intimate experience with deep learning frameworks (TensorFlow,  Pytorch, Caffe, Keras)
• Previously built and shipped ML products

Bonus Qualifications
• PhD in Computer Science with focus on Computer Vision
• Comfortable with speaking at tech / industry conferences. 

Responsibilities
• Build, implement, deploy computer vision algorithms that significantly speed up labeling
• Deliver product innovation in how teams using Labelbox are able to improve the accuracy of their model

We believe that AI has the power to transform every aspect of our lives -- from healthcare to agriculture. The exponential impact of artificial intelligence will mean that mammograms can happen quickly and cheaply irrespective of the limited number of radiologists in the world and that farmers will know the instant disease hits their crops without needing to be there in person.

We’re building a platform to accelerate the development of this future. Rather than requiring companies to create their own expensive and incomplete homegrown tools, we’ve created a training data platform that acts as a central hub for humans to interface with AI. When humans have better ways to input and manage data, machines have better ways to learn.

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.