Simbe is building the AI-powered operating system for physical retail. Our autonomous robots and multimodal computer vision platform turn complex, constantly changing stores into accurate, actionable intelligence for leading retailers around the world. Simbe combines robotics, computer vision, machine learning, data infrastructure, and customer-focused product design to help retailers improve shelf availability, price and promo execution, inventory accuracy, and store team productivity.
Simbe is looking for a Computer Vision / Applied AI Intern to join the team building and improving the computer vision systems that transform imagery collected by Tally into actionable retail intelligence.
This internship is designed for students who want hands-on experience applying computer vision and machine learning to real-world data and production systems at meaningful scale.
Why This Internship Is High Impact
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.
Responsibilities
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.
Required Qualifications
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.
Bonus Qualifications
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.
Internship Program Structure
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
Interns join Simbe as part of a structured cohort and work full time from our Burlingame, California headquarters.
Each intern will have:
We do not believe in creating busywork for interns. Your project will be selected closer to your start date based on active team priorities so that you can contribute to meaningful work alongside our full-time engineering team.
Result Driven — We are customer-centric and results-driven. We strive to create immense value for our team, partners, customers, and investors.
Empathetic — We are sensitive and mindful. We support each other in challenging times, both professionally and personally.
Transparent — We value open communication internally, and with our partners and customers. We are receptive to feedback.
Agile — We are eager to learn and adapt quickly to changes and customer needs.
Innovative — We are bold and innovative, with an intense focus on product design, user experience, and customer value.
Leaders — We strive for excellence. We are accountable, the best at what we do, and leaders in our field.
