Software Engineer Intern (ai/ml) - 2026

Snowflake Snowflake · Data AI · Warsaw, Poland · Engineering

Software Engineering Intern role at Snowflake, contributing to an AI-focused R&D team building AI-powered products like Snowflake Intelligence. The role involves designing, implementing, and maintaining AI systems, ML pipelines, and optimizing model serving and inference infrastructure.

What you'd actually do

  1. Design, implement, and maintain AI-powered systems and infrastructure for large-scale data processing and inference.
  2. Collaborate with AI researchers and product teams to bring novel ML methods into production.
  3. Develop robust, scalable, and efficient ML pipelines, from model training to deployment.
  4. Optimize model serving, latency, and cost efficiency in distributed environments.
  5. Contribute to high-quality, maintainable codebases, including testing, documentation, and CI/CD.

Skills

Required

  • Python, Go, or Java
  • ML frameworks (e.g., PyTorch, TensorFlow)
  • data processing systems (e.g., Spark, Snowpark)
  • distributed systems
  • model serving
  • inference infrastructure
  • clean, efficient, and scalable code
  • software development lifecycle
  • best practices
  • work independently
  • collaborating within a team
  • communication skills

Nice to have

  • AI-powered systems and infrastructure
  • novel ML methods into production
  • robust, scalable, and efficient ML pipelines
  • model training to deployment
  • optimize model serving, latency, and cost efficiency
  • testing, documentation, and CI/CD
  • code reviews
  • cross-team design discussions

What the JD emphasized

  • AI-native thinkers
  • AI as a high-trust collaborator
  • AI-focused R&D team
  • AI-powered products
  • AI innovation
  • AI/ML research projects
  • AI-driven R&D team
  • model serving
  • inference infrastructure
  • ML pipelines
  • model training to deployment
  • model serving, latency, and cost efficiency

Other signals

  • AI-powered products
  • AI innovation
  • AI-focused R&D team
  • ML frameworks
  • model serving
  • inference infrastructure
  • ML pipelines
  • model training to deployment