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
Artificial intelligence is having a profound impact on all aspects of our lives and is transforming how work is conducted in every industry. Today, AI systems are enabling businesses to personalize services, converse with customers, automate operations, optimize workflows, predict demand, and recommend next best actions. A common thread to our ambitious AI research agenda is to understand how AI systems and algorithms can be designed responsibly and produce effective outcomes for their enterprise users.
We are seeking intern candidates to help us advance our research and development agenda on artificial intelligence in areas including Natural Language Processing, Neuro-Symbolic AI, Human-Centered AI, Trusted AI, Knowledge and Reasoning, AI for Business Automation, AI Applications, AI Security, AI Hardware, Automated AI, Speech and Conversational AI.
You desire to work in a fast-paced research environment in close collaboration with world-class researchers, software engineers, and designers to create and maintain applications and infrastructure in support of our AI research agenda. You excel at verbal and written communications. You will deliver production-level code to support the commercialization of research assets.
Candidates must be willing to work in any of the following locations: San Jose, CA; Cambridge, MA; Yorktown Heights, NY
Required Technical and Professional Expertise
Candidates should have knowledge and experience in one or more of the following areas:
- Machine learning theory: discriminative models, generative models, deep neural networks, detecting and mitigating bias, adversarial robustness, causality, uncertainty
- Machine learning engineering: creating training pipelines and evaluating models using toolkits such as PyTorch, TensorFlow, and scikit-learn
- Experience publishing scientific results in technical communities such as NeurIPS, ICML, ICLR, IJCAI, ACL, AAAI, KDD, CHI, IUI, CSCW, or similar
- Software engineering best practices, including agile techniques
- Cloud-native development and toolkits such as Docker, Kubernetes, and OpenShift
- Experience in training large-scale machine learning models
- Qualitative and quantitative user research and user-centric design
- Experience solving analytical problems using rigorous and quantitative approaches
- Experience analyzing large-scale data from a variety of sources
- Design, validation, and characterization of algorithms and/or systems
- Experience in front and back-end web application development and frameworks such as HTML, CSS, Bootstrap, Carbon, React, Flask, Node.js, etc.
- Backend storage technologies such as SQL and NoSQL databases such as Postgres, MongoDB, Cloudant, ElasticSearch, etc.
Preferred Technical and Professional Expertise