MillerKnoll is looking to solve real-world problems using machine learning and generative AI, and is seeking an ML/AI Engineer intern to contribute to impactful solutions that serve the business.
Requirements
- Strong foundation in mathematics and algorithms, especially in linear algebra, probability, statistics, and optimization.
- Strong Python skills and experience with ML concepts and frameworks such as Scikit-learn, XGBoost, time series forecasting, PyTorch or TensorFlow, and Transformers.
- Familiarity with GenAI application development tools as LangChain, LlamaIndex, or similar frameworks, LLM APIs (OpenAI, Cohere, Anthropic, etc.), embeddings, prompt engineering, and RAG.
- Strong analytical and problem-solving skills with a proactive mindset.
- Excellent communication skills; able to explain technical concepts to diverse audiences.
- Strong collaboration skills—able to contribute in a highly interdisciplinary and agile setting.
Responsibilities
- Assist in designing, training, and evaluating machine learning, deep learning, and LLM-based models.
- Contribute to developing GenAI-powered applications using modern tools and frameworks.
- Support the team in implementing MLOps/LLMOps practices such as version control, experiment tracking, and model monitoring.
- Help build and maintain data pipelines and preprocessing workflows for training and evaluation.
- Prototype and test new AI/ML approaches to validate feasibility.
- Collaborate with engineers and data scientists to integrate models into APIs or user-facing tools.
- Document code, workflows, and findings for knowledge sharing.
Other
- Currently pursuing a degree in Computer Science, Data Science, Engineering, or a related field.
- You must have a GPA of at least 3.0 (overall or within your program) to qualify for consideration in the Intern Program.
- Must be authorized to work in the US and will not require sponsorship now or in the future.
- Must be able to perform all essential functions of the position with or without accommodations
- Must be able to commit to a 12-week full-time internship
