Intern, Machine Learning Developer

Autodesk Autodesk · Enterprise · Toronto, ON +1

Autodesk is seeking an AI/ML Intern to contribute to the development of their AI platform capabilities. The intern will build shared frameworks and tools for product teams to develop safe, observable, and scalable AI agents, focusing on context management, evaluation, observability, and responsible AI practices. The role involves research, prototyping, design, implementation, and collaboration with ML Engineers and Data Engineers.

What you'd actually do

  1. Research and prototype core AI platform components, including context/state SDKs, evaluation harnesses, and safety frameworks
  2. Design and implement observability and logging tools for tracing AI agent behaviour, cost, and performance metrics
  3. Develop data and model pipelines to support retrieval, prompt management, and consistent memory systems
  4. Support model evaluation and testing for reliability, factual accuracy, and drift detection
  5. Assist in creating guardrail and redaction APIs to ensure data safety and compliance in prompts and logs

Skills

Required

  • Python
  • ML frameworks (Scikit-learn, PyTorch, TensorFlow)
  • Data Engineering concepts (data validation, ETL pipelines, dataset versioning)
  • Machine Learning lifecycle workflows
  • model evaluation
  • experimental design
  • Git
  • cloud environments (AWS, Azure)

Nice to have

  • LLMs
  • RAG architectures
  • multi-agent systems
  • AI Observability tools (LangFuse, Weights & Biases, OpenTelemetry)
  • context and state management for AI agents (memory stores, embeddings, retrieval APIs)
  • prompt safety
  • PII redaction
  • governance frameworks
  • building shared ML tools and SDKs
  • semantic evaluation metrics
  • responsible AI practices

What the JD emphasized

  • safe, observable, and scalable AI agents
  • context management
  • evaluation
  • observability
  • responsible AI practices
  • guardrail and redaction APIs
  • responsible and reproducible AI development

Other signals

  • AI platform capabilities
  • shared frameworks and tools
  • safe, observable, and scalable AI agents
  • context management
  • evaluation
  • observability
  • responsible AI practices