Entrylevel Artificial Intelligence Engineer Jobs in Windham, ME

Job Description Hybrid -Westbrook, ME Job Description: The Machine Intelligence team in R&D is looking for an entry-level Data Scientist to develop machine learning solutions for the hematology analyzers. In this role, you will work on classification and clustering problems on tabular data, with solutions deployed on edge hardware in our analyzer platforms. You will work under the supervision of a senior data scientist who will guide your technical development and project execution.

We are looking for a curious, adaptable team player eager to build foundational skills in applied machine learning. What you can expect: Develop classification and clustering models on tabular data to support hematology analyzer capabilities Contribute to model development, evaluation, and iteration under the guidance of a senior data scientist Partner with senior team members to understand requirements, explore data, and validate model performance Document your work clearly so it can be reviewed, reproduced, and built upon by the team Deploy your solutions to edge hardware What you need to succeed: 0-2 years of experience applying machine learning to real-world problems (internships, research, and coursework projects count) Strong working knowledge of Python and common data science libraries (pandas, scikit-learn, NumPy) Solid foundation in statistics, machine learning, and algorithms Demonstrated understanding of classification and clustering methods for tabular data, including when to apply which approach and how to evaluate results Curiosity about the data and the underlying generating processes - a habit of asking "why" before reaching for a model A growth mindset and willingness to learn from more senior team members Ability to communicate analyses and results clearly to your immediate team Bachelor's degree in a quantitative field (statistics, computer science, math, engineering, or related); advanced degree a plus Nice to have: Exposure to deploying ML models on resource-constrained or edge hardware Familiarity with model optimization techniques (quantization, ONNX, TFLite) Experience with version control (Git) and collaborative software development practices Experience modeling data for medical, diagnostic or life sciences applications