Machine Learning and Data Science Engineering Intern

About the job

Turn your machine learning coursework into insight that helps AI infrastructure run more reliably.

As a Machine Learning and Data Science Engineer Intern, you will analyze telemetry from data center facilities and engineering infrastructure. You will apply data science and machine learning methods to real operational data.

Your work will help engineers spot patterns, understand anomalies and make better-informed decisions. You will learn how infrastructure data connects to reliability, performance and day-to-day operations.

You will clean and explore datasets, build reproducible analyses and evaluate predictive models. You will also create visualizations, document assumptions and share findings with technical colleagues.

This internship gives you practical experience with real-world infrastructure data, supported by engineers who value curiosity and clear thinking.

The team and culture

You will work with the Facilities Reliability Engineering team in Austin. The team supports infrastructure used to develop and test the next generation of AI systems.

Work happens through clear operational questions, shared data exploration, prototype analysis and practical review with engineers. Decisions are shaped by evidence, operational knowledge, reproducible results and honest discussion of uncertainty.

You will own defined tasks with guidance, feedback and room to ask questions. As an intern, you will build confidence by turning data into insight engineers can use.

What we’re looking for

• Current enrollment at junior or senior undergraduate level, or in a master's program, in data science, AI, machine learning, computer science, statistics or a related field
• Foundational knowledge of machine learning, statistics and data analysis through coursework, research, projects or practical experience
• Programming experience in Python, with familiarity using common data-analysis libraries
• Experience preparing, exploring, analyzing and visualizing datasets
• Understanding of supervised machine learning concepts, including regression, classification, model training and model evaluation
• Clear communication, a methodical approach to problem-solving and willingness to seek guidance when needed

While we have outlined a set of requirements, we value transferable skills and diverse experiences.

Benefits

• Flexible working: Balance your work and personal life with greater flexibility
• Comprehensive healthcare: Medical, dental and vision coverage to help keep you and your family healthy
• Phantom equity: Share in Graphcore’s success
• Tax-advantaged healthcare savings: Flexible Spending Accounts (FSAs) and Health Savings Accounts (HSAs) to help you make the most of your healthcare spending
• Peace of mind protection: Disability and life insurance to provide fin