About this role
Mill builds smart systems and infrastructure designed to turn food scraps into valuable resources like chicken feed, helping households and upcoming commercial operations divert waste from landfills. The computer vision team focuses on developing object detection, segmentation, and classification models for material and food recognition that operate directly on physical devices. Joining this team provides practical experience in tackling production challenges through dataset curation, model training, and the creation of reliable experiment pipelines.
Mentored by senior engineers, the incoming intern tackles real problems ranging from model evaluation to scalable experiment tracking and tooling. Ideal candidates bring a strong academic foundation in machine learning or computer vision along with practical Python skills. Those who thrive here enjoy translating theoretical research into dependable, shipped systems that impact everyday hardware.
What you will do
- Build and refine detection, segmentation, and classification models for material and food recognition tasks.
- Curate and quality-assure large image and video datasets by identifying labeling issues and failure modes.
- Develop and scale training pipelines and experiment-tracking frameworks to ensure reproducible results.
- Design and execute rigorous model evaluations to measure ongoing performance improvements.
- Collaborate closely with engineering and data teams to transition experimental models into production environments.
- Document methodologies and communicate research findings effectively to the broader team.
What you need
- Currently pursue or recently complete a degree in computer vision, machine learning, robotics, or a related technical field.
- Demonstrate strong programming proficiency in Python along with practical experience using deep learning frameworks.
- Possess a solid understanding of modern computer vision concepts including representation learning, segmentation, and object detection.
- Show comfort working with the complexities and noise of real-world datasets during model training and evaluation.
- Exhibit a clear interest in moving research skills into practical, deployed systems.
Nice to have
- Experience with MLOps and experiment-tracking tools such as ClearML, Weights & Biases, or MLflow
- Experience with dataset-management and curation platforms like FiftyOne, Voxel51, CVAT, or Roboflow
- Familiarity with data pipelines, cloud platforms like AWS or GCP, Docker, and Kubernetes
- Publications or open-source contributions in computer vision or machine learning
Benefits
- Potential equity grant
Skills and tech stack
Questions candidates ask
What is the remote and office policy for this role?
This is a hybrid role based in San Bruno, California. Candidates must be able to work from the designated location.
What is the compensation range for the internship?
The estimated base hourly range for this position is $35 to $45. Compensation decisions depend on factors such as skill sets, experience, training, and market conditions.
What tools and frameworks will I use on the team?
You will work with Python, deep-learning frameworks, and experiment-tracking or dataset-curation tools such as ClearML, Weights & Biases, MLflow, FiftyOne, and CVAT. Cloud infrastructure and containerization tools like AWS, GCP, Docker, and Kubernetes are also part of the technical environment.
Are there visa sponsorship opportunities available?
Applicants must be authorized to work for any employer in the U.S. The company is unable to sponsor an employment visa for this role.