Cisco AI Machine Learning Engineer PhD Intern United States 

United States, California, San Jose 

332421471

Applications are accepted until further notice.

Please note this posting is to advertise potential job opportunities.This exact role may not be open today but could open in the near future.

What You'll Do

are responsible fordeveloping and implementing applications based on generative large language models such as GPT-4, Claude, Llama, and their derivatives, ensuring they meet business requirements.You are expected to stay informed about the latest developments in AI and machine learning, and proactively suggest the adoption ofnew technologies. You will also design and develop APIs for smooth interaction with AI models. In addition, you will address and resolve any issues that arise from the AI applications. It is part of your duties to create clear and comprehensive documentation for the developed models and systems, making it accessible to both internal teams and external partners.

Who You Are

With strong analytical skills, you excel at interpreting data and have the ability to communicate complex information in a simple, actionable manner.

Minimum Qualifications:

• Recently completed or currently enrolled in a PhD program in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field

• Understanding of Deep Learning Architectures (Transformers, LSTMs, etc.).

• Experience with Transformer-based models (BERT, GPT, other Large Language models).

• Proficient in programming languages such as Go/Java, particularly for backend development and integration with data science applications.
  

Preferred Qualifications:

• Proficient with data science toolkits such as Pandas, NumPy, SciPy, and Scikit-learn, and knowledgeable in vector databases like Milvus or Pinecone for AI similarity searches.

• Skilled in fine-tuning large language models like GPT, BERT, and Llama-2 for domain-specific applications, and experienced with generative AI frameworks includingLangChainand RAGs.

• Practiced in the development and application of GANs (Generative Adversarial Networks) for creative AI solutions.

• Competent in Time Series Systems, with the ability to perform real-time series analysis and streaming data processing.

• Well-versed in cloud technologies such as Google AI Platform or AWS for scalable AI deployments, and in-depth knowledge of machine learning algorithms, including generative models.

• Familiar with big data technologies (e.g., Hadoop, Spark, Kafka) and machine learning frameworks (TensorFlow,PyTorch) for efficient model training and development.

• in advanced anomaly detection and pattern recognition within time series data,leveraginginsights from complex datasets

“old” (40 years strong) and only about hardware, but

But “Digital Transformation” is an empty buzz phrase without a culture that allows for innovation, creativity, and yes, even failure (if you learn from it.)