Ribbon Research Labs (RRL) is seeking a research-focused Artificial Intelligence Engineer Intern to help advance applied AI/ML and agentic AI capabilities for next-generation network applications. This role is ideal for MS/PhD students who enjoy rigorous problem formulation, experimentation, and translating novel research ideas into working prototypes that deliver measurable impact.
You will work with real-world datasets spanning LTE/5G mobile, IP transport networks, and voice/video systems to develop descriptive, predictive, and/or prescriptive models. The internship emphasizes research practices (literature review, baselines, ablations, reproducibility) and clear technical writing, with opportunities to contribute to internal technical reports and external publications/patent disclosures where appropriate.
Essential 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.
Experience and Skills:
• 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.
• Publication record (or strong potential) demonstrated via papers, posters, workshop submissions, open-source research contributions, or technical blog posts.
• Excellent written and verbal communication skills, including clear scientific/technical writing and the ability to explain methodology and results.
Please Note:
'All qualified applicants will receive consideration for employment without regard to race, age, sex, color, religion, sexual orientation, gender identity, national origin, protected veteran status, on the basis of disability, or other characteristic protected by applicable law.'
US Citizens and all other parties authorized to work in the US are encouraged to apply.