Intern, AI Data Developer (Winter) at Autodesk in Toronto, ON

Job description

Job Requisition ID # 26WD101082 Position Overview As an AI Data Developer Intern on the Advanced Compliance Products (ACP) team at Autodesk, you will help build and scale the data pipelines, models, and platform integrations that power ACP's compliance and analytics capabilities. You will work alongside data engineers, AI/ML engineers, and platform teams to design systems that ingest and process large volumes of telemetry, engineer features for scoring and classification models, and extend shared internal platforms with new domain-specific capabilities; all while ensuring data quality, reliability, and observability throughout the pipeline. The work we do at Autodesk touches nearly every person on the planet. By creating software for making buildings, machines, and even the latest movies, we influence and empower some of the most creative people in the world to solve problems that matter. Responsibilities · Design, build, and maintain data pipelines (ETL/ELT) that ingest, clean, validate, and transform large-scale telemetry and operational data · Engineer features from raw signals to support scoring, classification, or ranking models, and iterate on model performance against benchmark datasets · Build and maintain data models, schemas, and versioned datasets that other engineers and analysts can rely on · Evaluate model and pipeline output for accuracy, drift, and reliability, and implement monitoring/alerting to catch regressions early · Investigate how existing shared platforms and services are structured, identify coupling points and separation-of-concerns gaps, and propose or prototype ways to extend those platforms for new use cases · Design, prototype, or build a proof-of-concept extension or integration end-to-end in collaboration with platform stakeholders · Document data pipeline architecture, data contracts, and technical decisions to support handoff and future maintainability · Present findings, benchmarks, and recommendations to engineering and business stakeholders Minimum Qualifications · Currently enrolled in a full-time undergraduate degree program with expected graduation of April 2027 or later · Major in Computer Science, Engineering, Data Science, Statistics, or a related field · Proficiency in Python and SQL, including data manipulation and ML libraries (e.g., pandas, Scikit-learn, PySpark) · Solid understanding of data engineering fundamentals: ETL/ELT pipeline design, data validation, schema design, and dataset versioning · Understanding of Machine Learning lifecycle workflows, model evaluation (precision/recall), and experimental design · Experience with Git and familiarity with cloud environments and distributed data processing (e.g., AWS, Azure, Spark) · Hands-on experience using AI coding assistants (e.g., Cursor, Claude Code, GitHub Copilot) to write, debug, or refactor code · Comfortable working with ambiguous, outcome-based problems rather than a fixed task list Preferred Qualifications · Experience with large-scale/distributed data processing frameworks (PySpark, Spark SQL) against production-scale data · Experience with workflow orchestration and pipeline tooling (e.g., Airflow, dbt) and data warehousing concepts · Exposure to classification/scoring models, anomaly detection, or feature engineering on structured/behavioral signals · Familiarity with GenAI/LLM-powered platforms (e.g., internal chatbots, RAG-based BI tools) and their underlying architecture · Understanding of platform/service architecture and separation-of-concerns design in a multi-tenant shared codebase · Experience reverse-engineering or “diffing” black-box system outputs against raw inputs to infer underlying logic · Practical experience using AI tools (e.g., Cursor, Claude Code) for tasks beyond code generation such as codebase comprehension, test generation, documentation, and data analysis · Strong written communication skills for producing architecture docs, data contracts, and stakeholder-facing recommendations · Interest