ML & Data Science Engineer Intern — Telemetry Analytics

Graphcore Early Careers; Austin (TX); On-site; USD 28,000 - 41,000; Full time

Job summary

Graphcore Early Careers seeks a Machine Learning and Data Science Engineering Intern in Austin, TX. You will apply ML methods to telemetry from data center facilities, exploring real-time and historical data and communicating findings to support reliable operations.

The role involves developing prototypes, evaluating predictive analyses, and collaborating with experienced engineers to validate insights for informed engineering decisions.

Qualifications

• Foundational knowledge of machine learning, statistics, and data analysis.
• Programming experience in Python and familiarity with data‑analysis libraries.
• Ability to collect, explore, and visualize datasets.

Responsibilities

• Define operational questions with Facilities Reliability Engineering teammates that data analysis can answer.
• Collect, prepare, and explore telemetry datasets under guidance.
• Analyze real-time and historical telemetry to identify patterns and anomalies.
• Develop and evaluate predictive models for operational events or conditions.
• Apply statistical and ML methods to defined operational problems.
• Use Python to create reproducible analyses, experiments, and visualizations.
• Document model performance, assumptions, and limitations clearly.
• Support repeatable data-processing workflows for team members.

Skills

Python programming

Data analysis

Machine learning basics

Statistics

Communication

Education

Pursuing bachelor’s degree in CS/DS/AI or related

Pursuing master’s degree in CS/AI/DS or related

Tools

Pandas

NumPy

Scikit-learn

PyTorch

SQL

Job description

Graphcore Early Careers seeks a Machine Learning and Data Science Engineering Intern in Austin, TX. You will apply ML methods to telemetry from data center facilities, exploring real-time and historical data and communicating findings to support reliable operations.

The role involves developing prototypes, evaluating predictive analyses, and collaborating with experienced engineers to validate insights for informed engineering decisions.