Computer Vision Intern — Production-Ready ML (Hybrid)

Mill; San Bruno (CA); Hybrid; USD 48,000 - 62,000; Full time

Job summary

Mill is seeking a Computer Vision Intern to work with our growing CV team on real production problems, from dataset curation and model training to pipelines and experiment tooling that keep CV systems reproducible and fast to iterate.

This hybrid role is based in San Bruno, CA, with mentorship from senior engineers and opportunities to see your work on devices and users.

You'll gain hands-on experience applying research to practical applications in food waste prevention.

Qualifications

• Currently pursuing (or recently completed) a Degree in Computer Vision, Machine Learning, Robotics or a related field.
• Strong programming in Python and hands-on experience with a deep-learning framework.
• Experience training and evaluating models on real datasets, and comfort with the messiness of real-world data.
• A genuine interest in translating research skills into practical, shipped systems.

Responsibilities

• Build and improve computer vision models — detection, segmentation, and classification — for material and food recognition and related tasks.
• Curate, explore, and QA large image/video datasets, surfacing failure modes, duplicates, and labeling issues.
• Develop and scale training pipelines and experiment-tracking/reporting so experiments are reproducible and results are easy to compare — i.e., help build the "experiment harness" the team relies on.
• Design and run rigorous model evaluations and contribute to how we measure progress.
• Collaborate with engineering and data teams to move promising results toward production.
• Document your methods and share findings with the team.

Skills

Python

Deep learning

Dataset training

Model evaluation

Research translation

Education

Degree in Computer Vision

Tools

ClearML

Weights & Biases

MLflow

Docker

Kubernetes

Job description

Mill is seeking a Computer Vision Intern to work with our growing CV team on real production problems, from dataset curation and model training to pipelines and experiment tooling that keep CV systems reproducible and fast to iterate.

This hybrid role is based in San Bruno, CA, with mentorship from senior engineers and opportunities to see your work on devices and users.

You'll gain hands-on experience applying research to practical applications in food waste prevention.