Machine Learning Engineer -- Intern

Role Overview

We’re looking for an Machine Learning Engineer Intern to join our paid Summer 2026 internship cohort. The right person will be excited to help build AI-native developer tools. You will contribute to ML projects across dataset preparation, model experimentation, benchmarking, and exploring new frameworks or inference toolchains.

What You’ll Do:

• Train, evaluate, and debug machine learning models (e.g., deep learning, classical ML, multimodal models) using Python, PyTorch, and related frameworks.

• Use our internal AI-powered tooling to accelerate model development, dataset preparation, experiment tracking, and deployment workflows.

• Help test features like dataset validation, automated hyperparameter search, model introspection, and inference/runtime integrations.

• Provide structured feedback on usability, model behavior, edge cases, and failure modes (you’re part of the product loop).

• Build demo models, evaluation scripts, or experiment workflows that help us validate reliability and usability of the platform.

• Read academic papers, model cards, and technical documentation to cross-verify model performance and expected behavior.

You'll be a good fit if you:

• Have hands-on experience training ML models (vision, NLP, or embedded/edge ML all welcome).

• Know your way around core ML concepts: model architectures, loss functions, optimization, evaluation metrics.

• Have experience with ML toolchains and workflows (e.g., PyTorch Lightning, Hugging Face, ONNX, TensorRT, Weights & Biases).

• Are curious about how AI development tools could be radically better—and want to help shape that future.
  

Ideal candidates will:

• Have a Master’s in Mathematics, Data Science, or Engineering.

• Bring prior work or internship experience with model training, ML research, or applied AI engineering.

• Be hungry to contribute to an ambitious startup, with opportunities to go full-time