Computer Vision / Applied AI Intern — Summer 2027

Work with real-world visual data. Help improve systems that interpret imagery collected by autonomous robots operating in live retail environments.

Learn production computer vision. Gain exposure to the full lifecycle of CV/ML systems, from datasets and experimentation through evaluation and production deployment.

Contribute to meaningful work. Your project will align with an active team initiative rather than being isolated “intern work.”

Own a defined project. You’ll be given clear goals and an appropriately scoped problem you can meaningfully contribute to during the internship.

Learn from experienced CV and ML engineers. You’ll work directly with Simbe engineers and have a dedicated mentor separate from your hiring manager.

See how models perform in the real world. Work with the challenges that come from noisy imagery, changing retail environments, data quality, edge cases, and production requirements.

Own and contribute to a scoped project aligned with the Computer Vision / Applied AI team’s priorities during your internship.

Develop, test, and evaluate computer vision and machine learning solutions using Python and modern ML frameworks.

Work with production image datasets and help identify patterns, failure modes, and opportunities for improvement.

Contribute to areas such as object detection, OCR, barcode decoding, segmentation, dataset curation, annotation quality, model evaluation, or error mining depending on current team needs.

Build or improve tools that support dataset analysis, model evaluation, experimentation, or production workflows.

Write maintainable, well-structured code and contribute tests where appropriate.

Use quantitative analysis to evaluate model performance and communicate findings clearly.

Collaborate with engineers across Computer Vision, Applied AI, Data, Robot Software, and other technical teams.

Communicate progress, technical decisions, challenges, and results throughout the internship.

Present your project, approach, results, and key learnings at the end of the program.

Currently pursuing a BS, MS, or PhD in Computer Science, Computer Engineering, Robotics, Machine Learning, Data Science, or a related technical field.

We primarily target students in their final year of study and will also consider strong juniors.

Strong Python programming skills, including familiarity with common data structures, algorithms, and software development practices.

Coursework or hands-on experience in computer vision, machine learning, or deep learning.

Experience with PyTorch, TensorFlow, or a comparable machine learning framework.

Experience working with structured Python projects beyond notebooks alone.

Familiarity with Git or another source-control system.

Comfortable working with data to evaluate results, investigate failures, and support technical decisions.

Strong technical problem-solving skills and willingness to work through ambiguous or unfamiliar problems.

Strong communication and collaboration skills.

Personal computer vision, machine learning, robotics, or software projects outside of coursework.

Previous internship, research, or project experience involving computer vision or machine learning.

Experience with object detection, image segmentation, OCR, barcode recognition, image classification, or related vision problems.

Experience creating, cleaning, curating, or evaluating image datasets.

Familiarity with model evaluation techniques, error analysis, experiment tracking, or data-quality workflows.

Experience with pandas, NumPy, OpenCV, Jupyter, SQL, or related data and ML tooling.

Familiarity with Linux, shell scripting, Docker, or cloud-based development environments.

Experience with AI-assisted coding and engineering tools and the ability to thoughtfully validate generated output.

A defined technical project aligned with current team priorities

A hiring manager responsible for project scope, performance, and feedback

A dedicated mentor separate from the hiring manager

Structured midpoint feedback

Regular opportunities to collaborate with and learn from Simbe engineers

A final project presentation at the end of the internship