Customer Success Engineer Co-op Fall 2026

Introduction A Customer Success Engineer Co-Op (CSE) focused on IBM solutions means a career where you're helping clients fully realize the value of their existing products, whilst growing their adoption of next-generation technologies from across IBM's wider portfolio, including AI. This is a 16 week Co-Op position for actively enrolled undergraduate or graduate university students who are graduating between May 2027 and May 2028. Must be eligible and available for full time work (40 hours a week) during fall semester 2026. Students should not be enrolled in classes while working this Co-op. Preferring to 'show' vs. 'tell' we're looking for undergraduate and graduate students who want to combine their technical interests and education with the people skills needed to architect technical solutions, co-creating with customers, partners, and colleagues to solve technical and business challenges. IBM’s Sales Co-Op Program culture will set you up for success, whilst ongoing development will continue to advance your career through its upward trajectory. Our sales environment is fast-paced and supportive. Always part of a team, you'll be surrounded by leaders and colleagues who are always willing to help and be helped – as you support pilots that compel clients to invest in IBM's products and services. Your Role And Responsibilities A Customer Success Engineer Co-Op role in IBM has unique attributes. In addition to the people skills often associated with this position, in this CSM Co-Op role, you will work closely with customers, product managers, and development teams to understand client business requirements and implement solutions that address complex business challenges. You'll learn to co-create with colleagues and clients to deliver the engine that sits at the centre of their digital transformation's success. With a hands-on approach to coding, demonstrations and clear communication, you will be able to showcase IBM Ai solutions. You will design and articulate AI architectures compatible with a client’s technology stack via use-case identification, solution architecture design, and MVP (minimal viable product) builds. With technical expertise and a consultative style, you'll quickly build credibility as a trusted advisor at all levels with customers and IBM colleagues. To drive expansion and renewal growth, you'll guide leading enterprise companies through AI approaches that realize the full value of expanding their adoption of watsonx. Your primary responsibilities will include, but not be limited to: • Understanding Client's Data and AI Challenges and Building Trust: Understand clients' primary Data and AI challenges and establish yourself as a trusted technical expert for their migration, deployment, and adoption of AI and Hybrid Cloud offerings. • Facilitating Use Case Exploration and Business Framing: Lead use case exploration and business framing workshops, develop client value realization models. • Developing AI Solutions: Using your Data and AI engineering skills to code/ build MVPs using python, open source frameworks, RedHat and IBM watsonx. • Leading Persuasive Technical Conversations: Lead technical discussions that persuade clients to act based on their requirements and the value provided by IBM's solutions. • Creating Post-Deployment Customer Success Plans: Develop customer success plans aimed at continually increasing active user adoption of IBM's products. To Be Successful In This Role, You Will Need • Confidence to contact and engage potential new customers and deliver an elevated experience. • A desire to continually learn new technologies and how to apply their value in a client environment. • Motivation to achieve technical objectives and high client satisfaction. • Embrace curiosity and a growth mindset. You may work with any of the following technologies: Data, Artificial Intelligence, IT Automation, Cloud, and Security. This is a full-time, on-site position located in one of the following IBM offices: Brookhaven, GA, Chicago, IL, Dallas, TX, New York, NY, San Francisco, CA, and Washington, DC.  Readiness to travel, if needed. Required Technical And Professional Expertise • Actively Enrolled: Registered undergraduate or graduate university student eligible and available for full time work (40 hours a week) during fall semester 2026. Students should not be enrolled in classes while working this Co-op. • Technical Education: Seeking B.S. or M.S. degree in Computer Science, Artificial Intelligence, Data Science, Engineering, Information Systems, or equivalent technical degree/experience. • Programming Language: Experience with Python, notebooks. • AI Skills: Experience in Machine Learning, Deep Learning, Large Language Models. • Proficiency in Python and ML toolchains (NumPy, Pandas, Scikit-learn, Jupyter) to prototype models and data pipelines. • Hands-on experience with model deployment (e.g. serving Hugging Face models via APIs) and LLM inference (batch vs. real-time). • Understanding of model fine-tuning and in-context learning (choosing when to prompt-engineer vs. train) as part of the LLM lifecycle. • Specialized Agentic AI frameworks: LangChain, LlamaIndex • Deep Learning Frameworks: PyTorch, TensorFlow (including Keras), Hugging Face transformers • Vector Databases: Experience with vector stores like Pinecone, Weaviate, Milvus • Client Focused: Asks open-ended questions and understands needs to address business challenges. • Tech Savvy: Conversant about technology, latest industry trends and how it is being applied to address business challenges. • Team Player: Demonstrates team collaboration and can navigate different communication styles. • Excellent Communication Skills: Possess verbal, written, and interpersonal skills that are engaging, compelling and influential. • Self-Motivation and Problem-Solving Aptitude: A natural inclination toward self-motivation and initiative, in addition to the ability to navigate data and people to find answers and present solutions. Preferred Technical And Professional Experience • Familiarity with agile development methodologies. • Knowledge of AI and data governance. • Knowledge of generative AI architectures such as RAG, chatbots. • Experience with RedHat OpenShift and Kubernetes.