We’ll trust you to:

•  Interact with data engineers and ML experts across the company to understand their workflows and requirements to inform the next set of features for the platform.
•  Provide GPU management solution to enhance distributed training performance and resource usage efficiency
•  Enhance distributed training user experience using main stream and internal training frameworks
•  Design seamless workflow from model training to model inference
•  Troubleshoot and debug user issues
•  Provide operational and user facing documentation
•  Provide performance analysis and capacity planning for clusters

What we are looking for:

•  Have a strong sense of curiosity to solve new problems and keep learning new technologies.
•  Have a passion for providing reliable and scalable infrastructure
•  Experience building and scaling Docker-based systems using Kubernetes, Swarm or Mesos
•  Experience with ML infrastructure open source projects such as Kubeflow, Triton, MLFlow, Feast
•  Experience with mainstream machine learning frameworks such as Pytorch, Tensorflow
•  Experience with distributed systems eg. Kubernetes, Kafka, Zookeeper, Spark
•  Proficiency in two or more languages (Go, Python, C++, Java, or JavaScript) and willingness to learn more as needed
•  At least 2 years of experience as a software engineer

Nice to haves:

•  Experience working with authentication & authorization systems such as Spiffe and Spire
•  Experience with data encryption
•  Experience working with GPU compute software and hardware
•  Ability to identify and perform OS and hardware-level optimizations
•  Open source involvement such as a well-curated blog, accepted contribution, or community presence
•  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)
•  Passion for education e.g. providing workshops for tenants