AI Engineering Internship

wyzecam.comKirkland, WA

About The Position

We're looking for AI Engineer Interns to work alongside our AI team designing, building, and optimizing the machine learning systems that power Wyze products. You'll get end-to-end exposure across the AI lifecycle, from data and model development through deployment and evaluation, and ship work that reaches real users. This is a hands-on role for someone who wants to build, not just prototype.

Requirements

  • Currently pursuing a Master's or Ph.D. in Computer Science, Data Science, Engineering, AI, or a related field.
  • Strong programming fundamentals (Python preferred) and fluency using AI coding agents to work effectively and ship faster.
  • Working understanding of machine learning concepts, model evaluation, and how to reason about model behavior.
  • Familiarity with a modern deep learning framework (such as PyTorch) and an interest in LLM application development.
  • Strong problem-solving skills and genuine curiosity to dig into hard, ambiguous problems.
  • Ability to work both independently and collaboratively in a fast-paced environment.
  • Passion for smart home technology and a real interest in shipping practical AI.

Nice To Haves

  • Experience with cloud platforms (AWS, GCP, or Azure) for model deployment and scaling.
  • Exposure to computer vision, edge/on-device ML, or model optimization (quantization, distillation).
  • Hands-on experience building or evaluating LLM agents or multi-step AI workflows.

Responsibilities

  • Build and help deploy machine learning systems for computer vision and generative AI features, with attention to the efficiency and on-device constraints that define Wyze products.
  • Develop and experiment with LLM-powered and agentic workflows, including prompting, tool/API orchestration, and retrieval-based approaches.
  • Design and implement scalable, efficient ML pipelines for training, inference, and serving.
  • Build evaluation harnesses and feedback loops to measure model quality, catch regressions, and quantify the impact of changes.
  • Test, validate, and debug models across real-world use cases.
  • Document your work clearly so experiments are reproducible and decisions are easy to follow.
  • Track advancements in ML and AI and bring recommendations back to the team.
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