Hybrid Cloud Software Engineer Summer Intern 2022 (Graduate)
Introduction
At IBM, work is more than a job – it’s a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you’ve never thought possible. Are you ready to lead in this new era of technology and solve some of the world’s most challenging problems? If so, lets talk.
Your Role and Responsibilities
IBM Research is seeking graduate intern candidates with proven interest and experience in implementing innovative software solutions and applications. You will work in close collaboration with other researchers and engineers to create, maintain and support world-class applications and/or infrastructure. You will also deliver production level-code to support the commercialization of the resulting assets. Demonstrated communication skills are essential.
Candidates should have basic knowledge in one or more of the following skills:
-Computer architecture
-Computer network and communication libraries
-Cloud system software – virtualization, VMs, container, Openshift/Kubernetes
-Accelerator–GPU or FPGA architecture/programming
-Programming skills (C/C++, Java, Python, Javascript, Node.js, etc.)
-Building cloud applications using APIs and services
-Performance/scalability analysis
-Machine learning or HPC workload knowledge
-Software engineering practices including agile techniques (GitHub, Travis, Artifactory)
-System building/debugging/testing skills (Test coverage tools, Chaos Engineering)
-Programming on GPUs
Candidates must be willing to work in any of the following locations: Albany, NY; San Jose, CA; Cambridge, MA; Yorktown Heights, NY. Pursuing a Master’s or PhD degree in Computer Science, Computer Engineering or a related area is required.
Required Technical and Professional Expertise
- Programming skills (C/C++, Java, Python, Javascript, Node.js, etc.)-2 years
- Computer architecture – system building/debugging/testing skills-2 years
- Machine Learning-1 year
- Cloud native technologies (e.g. OpenShift, Kubernetes, OCP Satellite, Serverless)-2 years
- Software engineering practices including agile techniques (e.g. GitHub, Travis, Artifactory)-2 years
- Networking-2 years
Preferred Technical and Professional Expertise
- Computer network and communication libraries–2 years
- Cloud system software–virtualization, VMs, container, Openshift/Kubernetes–2 years
- Accelerator–GPU or FPGA architecture/programming-2 years
- Performance/scalability analysis-2 years
- Machine Learning or HPC workload knowledge-2 years
- AI Scaling platforms (Ray, Spark, KubeFlow, MPI)-2 years
- Timeseries analysis (metrics/logs/topology data, online learning, causality)-2 years
- Publication experience
- Demonstrated verbal and written communication skills