We’ll trust you to:

• Interact with Data Engineers and ML experts to understand their workflows and requirements to inform the next set of features for Platform Services
• Provide GPU management solution to enhance distributed training performance and resource usage efficiency
• Enhance user experience using main stream and internal training frameworks
• Design seamless workflows from model training to model inference
• Solve and debug user issues
• Provide operational and user facing documentation
• Provide performance analysis and capacity planning for clusters

You'll need to have:

• 4+ years of programming experience with an object-oriented programming language (Go, Python, C++, Java, or JavaScript)
• A degree in Computer Science, Engineering or similar field of study or equivalent work experience
• Experience with distributed systems eg. Kubernetes, Kafka, Zookeeper, Spark
• Experience with mainstream machine learning frameworks such as Pytorch and Tensorflow
• Experience building and scaling Docker-based systems using Kubernetes, Swarm or Mesos
• Have a strong sense of curiosity to solve new problems and keep learning new technologies

We'd love to see:

• Experience with ML infrastructure open source such as Kubeflow, Triton, MLFlow, Feast
• Knowledge of authentication & authorization systems such as Spiffe and Spire
• Experience with cloud providers such as AWS, GCP or Azure
• Experience with configuration management systems (Chef, Puppet, Ansible, or Salt)
• Experience with continuous integration tools and technologies (Jenkins, Git, Chat-ops)
• Experience with data encryption
• Experience working with GPU compute software and hardware
• Ability to identify and perform OS and hardware-level optimizations