ML Pipeline Engineer

New York, NY, United States


Role Location

  • New York, NY, United States


26 - 50 people


20 Jay St Ste 312
Brooklyn, NY, 11201, US

Tech Stack

  • Go
  • Node.js
  • Kubernetes
  • React

Role Description

Paperspace is looking for experienced machine learning pipeline engineers. We are building tools for machine learning developers and teams to rapidly deploy training and inference pipelines. Our Gradient platform is a continuous integration and deployment platform for machine learning that can be deployed quickly in the public cloud, or on-site in a private cloud. Our platform is targeting the needs of a single developer trying to train and publish an innovative new model, all the way up to a large company deploying critical line-of-business applications incorporating continuous learning stages and continuous model improvements. We feel that collaboration on model development, composition of models, and automation of training and deployment is the future of machine learning. Come help us build this future.

Paperspace... - is creating a serverless machine learning/deep learning platform which supports all popular machine learning frameworks - also provides cloud-based GPU desktops and servers for even more customizable pipeline configurations - is building on and committed to Kubernetes, containers, and open declarative cloud configuration tools - is focused on streamlining the definition and evolution of machine learning pipelines - is working with multiple hardware providers to integrate the latest machine learning technology and make it easily accessible

You will bring... - experience building a real production machine learning pipeline, including feature engineering, training, model management and inference stages - a deep understanding of machine learning/deep learning techniques, how to apply them, and how to scale them - experience with one or more machine learning frameworks, such as PyTorch, Tensorflow, Caffe, Keras - hands-on experience with multiple hyper-parameter optimization approaches - intermediate to advanced knowledge of Python - devops background and fluency in setting up production infrastructure for machine learning applications - fluency in the language and tools of machine learning, to be able to communicate with our developer, data scientist, and idev ops customers

It would be great if you... - have experience with inference farm deployments for real-time applications - GPU programming and optimization experience - understand the computational challenges involved in optimizing training efforts - experience with deploying and customizing streaming and batch oriented big data - have worked with novel distributed file and object store architectures, such as Ceph, Gluster, and S3 - have built continuous integration/continuous deployment frameworks, or worked with tools to implement those functions

About Paperspace

Paperspace provides an elegant GPU compute platform designed to eliminate infrastructure bottlenecks for developers.

Our Gradient product is CI/CD for Machine Learning: A popular toolkit for developing and deploying Deep Learning models. Teams of all sizes use Gradient to iterate faster and collaborate on intelligent, realtime prediction engines.

Paperspace is backed by leading investors including Y Combinator, Initialized Capital and Data Collective.

Our mission is to make cloud computing more accessible through radical simplicity, community-driven technical resources, and straightforward pricing.

Company Culture

Our culture is open, respectful, and flexible. Everyone is passionate about being on the team and making a huge impact though our efforts. We challenge each other when needed, but we don't have a hierarchy of people who know more, and those that know less. We respect everyone's opinions, especially during the planning process, and we encourage everyone to contribute. We also enjoy socializing after a major release, or sometimes just having a random lunch all together. Our environment is very informal. You will see that we care as much about each other as we do our product.

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