AI Research Intern – Ribbon Research Labs… at Ribbon… · Westford

Summary: Ribbon Communications is seeking a research-focused Artificial Intelligence Engineer Intern to help advance applied AI/ML and agentic AI capabilities for next-generation network applications. The role involves formulating research questions, designing experiments, and building prototypes using real-world datasets, while emphasizing research practices and technical writing. Responsibilities: - Formulate research questions from real network and product challenges; survey relevant literature and define success metrics, baselines, and evaluation protocols - Design and run hypothesis-driven experiments for analytical, machine learning, and agent-based (single- and multi-agent) systems; perform ablations, error analysis, and robustness testing - Build prototypes that operationalize research outcomes using customer datasets, open-source tools, and the Ribbon ecosystem; emphasize reproducible pipelines, clear documentation, and well-structured code - Communicate results and tradeoffs through crisp write-ups and presentations (e.g., experiment reports, technical memos, poster-style summaries) for both technical and non-technical stakeholders Required Qualifications: - Currently pursuing a MS or PhD in Artificial Intelligence, Data Science, Computer Science, or a related field - Exposure to networking/telecom concepts - Research or applied research experience across the full workflow: problem definition, data exploration, feature design, modeling, evaluation design, statistical rigor, and reproducible experiment tracking - Strong Python skills for research and prototyping, including model training/evaluation, clean APIs, testing, and performance debugging; familiarity with PyTorch and/or TensorFlow is a plus - Experience with modern deep learning and/or LLM-based systems (e.g., fine-tuning, retrieval-augmented generation, tool use, agentic/multi-agent patterns) and a practical approach to evaluation (offline metrics, human review, and failure-mode analysis) - Demonstrated ability to independently drive a research problem end-to-end (scoping, experimentation, iteration, and synthesis) with minimal supervision - Software engineering fundamentals for research code (version control, modular design, documentation); Familiarity with experiment tracking tools (e.g., MLflow, Weights & Biases) - Working knowledge of data wrangling (e.g., pandas, numpy, scikit) and data systems (schemas, SQL, structured and semi-structured data); Comfortable working with large, messy, real-world datasets - Experience working in Linux environments; familiarity with cloud platforms and containers (Docker/Kubernetes) for scalable training/inference is a plus - Excellent written and verbal communication skills, including clear scientific/technical writing and the ability to explain methodology and results Preferred Qualifications: - Publication record (or strong potential) demonstrated via papers, posters, workshop submissions, open-source research contributions, or technical blog posts Required Skills: Artificial Intelligence, Data Science, Computer Science, Networking, Telecom concepts, Research methodology, Python, PyTorch, TensorFlow, Deep learning, Large language models, Software engineering, Version control, Experiment tracking, Data wrangling, Pandas, NumPy, Scikit-learn, SQL, Linux, Cloud platforms, Containers Benefits: Eligibility for an incentive plan, Comprehensive benefits