Machine Learning and Data Science Engineering Intern

Machine Learning and Data Science Engineering Intern Austin, Texas, United States About Graphcore Graphcore is a global leader in artificial intelligence computing systems. We design advanced semiconductors and data center hardware that provide the specialized processing power needed to advance AI while improving the efficiency required for broad adoption. As part of SoftBank Group, Graphcore belongs to a family of companies developing transformative technologies. Our AI Engineering Campus in Austin supports the infrastructure used to develop and test the next generation of AI systems. The Opportunity As a Machine Learning and Data Science Engineer Intern, you will apply data science and machine learning methods to operational telemetry from data center facilities and engineering infrastructure. Working with experienced engineers, you will explore real-time and historical data, develop and evaluate predictive analyses, and communicate findings that can support reliable operations and informed engineering decisions. The internship combines practical analysis, prototype development, and exposure to real-world infrastructure data. What You Will Do Work with Facilities Reliability Engineering team members to define operational questions that can be addressed through data analysis and machine learning. Collect, prepare, clean, and explore telemetry datasets under the guidance of experienced team members. Analyze real-time and historical telemetry to identify patterns, trends, correlations, anomalies, and data‑quality issues. Develop and evaluate predictive models that may help anticipate operational events, trends, or conditions. Apply appropriate statistical, data science, and machine learning methods to clearly defined operational problems. Use Python and relevant libraries to create reproducible analyses, experiments, visualizations, and prototype solutions. Evaluate model performance using appropriate metrics and document the assumptions, limitations, and uncertainty of each approach. Support repeatable data‑processing and analytics workflows that other team members can understand and reproduce. Collaborate with engineers to validate analytical findings against operational knowledge and incorporate feedback into subsequent work. Present progress, findings, and recommendations clearly and seek guidance when encountering unfamiliar or complex problems. What You Will Bring Current enrollment at the junior or senior level in a bachelor's degree program, or enrollment in a master's degree program, in data science, artificial intelligence, machine learning, computer science, statistics, or a closely related field. Foundational knowledge of machine learning, statistics, and data analysis gained through coursework, research, projects, or practical experience. Programming experience in Python and familiarity with 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. A methodical approach to technical problems and the ability to use data to support conclusions. Clear written and verbal communication skills, including the ability to explain analytical findings to technical colleagues. Ability to collaborate, respond constructively to feedback, organize assigned work, and seek guidance when needed. Preferred Qualifications Familiarity with time-series analysis, forecasting, or anomaly detection. Exposure to real-time, streaming, telemetry, or sensor data. Experience with Python data science or machine learning libraries such as pandas, NumPy, scikit‑learn, or PyTorch. Familiarity with SQL or other tools for querying and manipulating data. Experience creating data visualizations, dashboards, or concise technical summaries. Interest in data center infrastructure, facilities systems, infrastructure monitoring, reliability engineering, or operational analytics. These qualifications are helpful, not required. We encourage you to apply even if you do not meet every preferred qualification. Equal Opportunity and Accommodations Graphcore is an equal opportunity employer. We consider qualified applicants without regard to race, color, religion, creed, sex, pregnancy, sexual orientation, gender identity or expression, national origin, ancestry, age, disability, genetic information, veteran status, or any other status protected by applicable law. Graphcore is committed to an inclusive and accessible hiring process. If you need a reasonable accommodation to participate in the application or interview process, please let the recruiting team know. Candidate Privacy Personal information submitted during the recruiting process will be handled in accordance with Graphcore's applicable candidate privacy notices. Voluntary Self-Identification For government reporting purposes, we ask candidates to respond to the below self-identification survey.Completion of the form is entirely voluntary. Whatever your decision, it will not be considered in the hiringprocess or thereafter. Any information that you do provide will be recorded and maintained in aconfidential file. As set forth in Graphcore Early Careers’s Equal Employment Opportunity policy,we do not discriminate on the basis of any protected group status under any applicable law. If you believe you belong to any of the categories of protected veterans listed below, please indicate by making the appropriate selection.As a government contractor subject to the Vietnam Era Veterans Readjustment Assistance Act (VEVRAA), we request this information in order to measurethe effectiveness of the outreach and positive recruitment efforts we undertake pursuant to VEVRAA. Classification of protected categoriesis as follows: A "disabled veteran" is one of the following: a veteran of the U.S. military, ground, naval or air service who is entitled to compensation (or who but for the receipt of military retired pay would be entitled to compensation) under laws administered by the Secretary of Veterans Affairs; or a person who was discharged or released from active duty because of a service‑connected disability. 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