Machine Learning Engineer

Applications are accepted until 1/12/2026

Who We Are
Join us at Cisco to shape the future of enterprise AI. We are building an AI Platform Team focused on creating the next-generation foundation that powers intelligent, secure, and scalable AI solutions across Cisco's ecosystem. This team is at the forefront of bridging research innovation with real-world application, addressing some of the most fundamental challenges in AI from advancing large language model capabilities and building agentic frameworks to optimizing data pipelines and ensuring trustworthy AI deployment at scale.

As part of this pioneering effort, you'll collaborate with leading AI researchers, data scientists, and engineers to design, develop, and operationalize AI systems that drive measurable business impact for millions of users worldwide. If you're passionate about solving hard technical problems and shaping how AI transforms enterprise security and networking, this is where you'll make it happen.

What You Will Do
As a Machine Learning Engineer for our AI Software & Platform team, you will be responsible for ML model development and delivery:

• Model Training, and Evaluation: This encompasses the complete cycle of training, fine-tuning, and validating language models. You will be designing and adapting LLMs for use in virtual assistants, automated chatbots, content recommendation systems, etc. You will come up with evaluations for the solutions and iterate on improvements.
• Algorithm Development for Enhanced Language Understanding: Focusing on the development or refinement of algorithms to improve the efficiency and accuracy of language models and understanding and generation tasks.
• Experimentation with Emerging Technologies and Methods: Actively exploring new technologies and methodologies in language model development, including experimental frameworks and software tools.

Basic Qualifications

• Education & Experience: BA/BS with 3+ years or MS in machine learning, proven project portfolio.
• ML Systems & Algorithms: Strong background in ML engineering, deep learning, and statistical modeling; experience building scalable ML solutions.
• LLM Expertise: In-depth knowledge of Transformer-based architectures (e.g., GPT) and training/fine-tuning large-scale models; exposure to Agentic frameworks.
• NLP Skills: Data preprocessing (tokenization, stemming, lemmatization), handling ambiguity and context, and