AI Researcher

Cisco•San Francisco, CA

1d•$146,200 - $239,000

About The Position

At Foundation AI, we are leading frontier AI research across Cisco. Our mission is to advance the state of AI. We translate that research into purpose-built models, agentic systems, new algorithms, optimization methods, and scalable data processing techniques that solve real-world problems at scale. We explore new model architectures, pre-training and post-training methods, inference optimization, evaluation techniques, and data pipelines. Together, we are advancing AI at Cisco and for the broader AI community! As an AI Researcher, you will work on the research, development, and deployment of advanced AI systems. Your focus will span large language models, agentic AI, multimodal learning, and enterprise-scale AI applications. We are looking for researchers who will help translate frontier research into practical systems by designing experiments, developing new methods, and turning research insights into impactful, scalable AI solutions. We are particularly interested in researchers with deep expertise in algorithm design for pre-training and post-training, scalable data curation, and reliable AI infrastructure. You will drive improvements to training algorithms, curate and optimize data at scale, and design infrastructure needed to train purpose-built models that can compete with frontier models on specific tasks aligned with Cisco's business priorities and Foundation AI's research agenda. We place a strong emphasis on publishing research and contributing meaningfully to the AI community. In this role, you will write scientific articles for top-tier machine learning and AI conferences, publish technical blog posts, and contribute to the open-source AI community!

Requirements

Bachelors + 2 years of related experience, or Masters + 0 years of related experience in Computer Science, Artificial Intelligence, Machine Learning, or a related field
Strong experience with Python, PyTorch, production-grade software engineering practices, and common AI/ML libraries
Experience with large language models, model training, fine-tuning, evaluation, inference, and optimization
Strong understanding of data pipelines, model experimentation, and scalable AI system design
Strong communication skills, with the ability to explain complex research and engineering concepts to cross-functional partners

Nice To Haves

Experience designing scalable data curation methods and building reliable infrastructure for large-scale model training, evaluation, inference, and deployment
Experience deploying high-performance inference engines such as vLLM, NVIDIA Triton, or TorchServe, and working with cloud-native deployment, Docker, Kubernetes, MLOps pipelines, and major cloud platforms such as AWS, GCP, or Azure
Demonstrated history of publishing research in top-tier AI/ML conferences such as NeurIPS, ICML, ICLR, or ACL, or contributing to significant open-source AI projects
Experience designing and scaling agentic AI workflows, multi-agent frameworks, and autonomous AI systems
Familiarity with cybersecurity principles, AI systems for security-focused use cases, safety, and robust machine learning

Responsibilities

Develop scalable data curation and processing methods that improve model training and task-specific performance.
Build production-ready AI systems using strong software engineering practices.
Optimize large language models and agentic systems for enterprise and security-focused use cases.