空间智能实习生/Spatial Intelligence Intern
Delft, Netherlands
About Caper
Caper (跃氪) is a pioneering company in retail automation technology under Instacart, a North American NASDAQ-listed company. As its core smart hardware brand, Caper is committed to redefining the shopping experience through groundbreaking product design and engineering innovation.
Our flagship product, the Caper Smart Cart, stands as a benchmark for embodied AI technology applications. To achieve seamless and reliable autonomous shopping functionality, we integrate cutting-edge AI vision systems, high-precision sensors, and advanced visual-language model (VLM) algorithms.
The Caper Smart Cart has successfully served both North American and global markets, including dozens of top-tier retail brands such as the top 3 supermarket giants in North America, demonstrating the excellence of our technology and product design. Backed by our parent company Instacart—a global leader in online fresh grocery delivery with over a decade of experience serving 600+ retail brands and 55,000+ stores across North America—Caper benefits from unparalleled market insights, technical resources, and large-scale implementation platforms, providing engineers with vast opportunities to tackle real-world complex challenges.
Welcome to visit our official website: www.caper.ai
About the Team
The Spatial Intelligence team builds the core AI technologies that enable smart shopping carts to understand and interact with retail environments. Our mission is to create a persistent Retail World Model that powers real-time cart localization, item localization, shopping assistance, and future autonomous retail robotics. We combine computer vision, localization, semantic understanding, and AI to build a spatial representation of the physical world that continuously evolves as the store changes.
As a Spatial Intelligence intern, you will work on scoped, hands-on projects alongside experienced engineers. You will contribute to real systems used in production retail environments—not toy demos—and gain exposure to how ML experimentation, computer vision QA, and evaluation infrastructure come together in an embodied AI product.
Internship details: 1 openings · 3 months · 3–4 days/week (~24–32 hours)
Project Areas
Interns are assigned a primary project and a secondary project in a complementary domain. Example project areas include:
- Item Location — Fine-tuning vs. Retrieval — Implement, compare, and benchmark fine-tuning and retrieval-based approaches for item localization.
- Side Camera Image Quality Tests — Define image quality metrics and build automated test suites for side-facing cameras.
- Location Toolbox Automation — Automate software tests for location-related modules.
- Item Location Evaluation Pipeline — Build pipelines for annotation, validation, and metric reporting.
Responsibilities
Depending on your assigned project, you may:
- Read relevant papers or internal docs and translate ideas into working prototypes.
- Prepare datasets, run baseline experiments, and track results in a reproducible way.
- Build or extend test automation for perception and localization software.
- Define and implement image quality or model evaluation metrics.
- Develop pipeline components for annotation, validation, and reporting.
- Write clear documentation and present progress to the team.
- Collaborate with engineers across localization, perception, and platform teams.
About You
Minimum Qualifications
- Currently enrolled in (or recently graduated from) a Computer Science, Engineering, Data Science, or related program.
- Solid programming fundamentals in Python (JavaScript a plus).
- Comfortable using Git, Linux shell, and collaborative development tools.
- Able to read technical material and implement solutions with mentor guidance.
- Clear written and verbal communication in English.
- Curious, self-motivated, and willing to ask questions.
Preferred Qualifications
- Coursework or project experience in machine learning, computer vision, or robotics.
- Familiarity with PyTorch or similar ML frameworks.
- Experience with test automation, CI pipelines, or QA tooling.
- Exposure to camera systems, image processing, or data pipelines.
- Prior internship, coursework project, or open-source contribution demonstrating initiative.
We do not expect production ML experience or advanced degrees. Strong fundamentals, learning ability, and interest in the problem space matter most.
What We Offer
- Direct mentorship from Spatial Intelligence engineers with weekly 1:1 check-ins and code reviews.
- Hands-on work on problems tied to live smart-cart technology.
- Flexible part-time schedule (3–4 days/week) suitable for students.
- Access to team learning resources and a collaborative, low-politics engineering culture.
Who Should Join Us
We are looking for motivated students and early-career builders who enjoy real hardware and real-world AI problems, learn quickly, communicate clearly, and are excited about retail automation and embodied AI. Every day we solve complex problems to create a shopping experience that feels effortless for users. If you want to grow as an engineer while contributing to technology deployed in stores worldwide, we'd love to hear from you.
About Caper
Caper (跃氪) is a pioneering company in retail automation technology under Instacart, a North American NASDAQ-listed company. As its core smart hardware brand, Caper is committed to redefining the shopping experience through groundbreaking product design and engineering innovation.
Our flagship product, the Caper Smart Cart, stands as a benchmark for embodied AI technology applications. To achieve seamless and reliable autonomous shopping functionality, we integrate cutting-edge AI vision systems, high-precision sensors, and advanced visual-language model (VLM) algorithms.
The Caper Smart Cart has successfully served both North American and global markets, including dozens of top-tier retail brands such as the top 3 supermarket giants in North America, demonstrating the excellence of our technology and product design. Backed by our parent company Instacart—a global leader in online fresh grocery delivery with over a decade of experience serving 600+ retail brands and 55,000+ stores across North America—Caper benefits from unparalleled market insights, technical resources, and large-scale implementation platforms, providing engineers with vast opportunities to tackle real-world complex challenges.
Welcome to visit our official website: www.caper.ai
About the Team
The Spatial Intelligence team builds the core AI technologies that enable smart shopping carts to understand and interact with retail environments. Our mission is to create a persistent Retail World Model that powers real-time cart localization, item localization, shopping assistance, and future autonomous retail robotics. We combine computer vision, localization, semantic understanding, and AI to build a spatial representation of the physical world that continuously evolves as the store changes.
As a Spatial Intelligence intern, you will work on scoped, hands-on projects alongside experienced engineers. You will contribute to real systems used in production retail environments—not toy demos—and gain exposure to how ML experimentation, computer vision QA, and evaluation infrastructure come together in an embodied AI product.
Internship details: 1 openings · 3 months · 3–4 days/week (~24–32 hours)
Project Areas
Interns are assigned a primary project and a secondary project in a complementary domain. Example project areas include:
- Item Location — Fine-tuning vs. Retrieval — Implement, compare, and benchmark fine-tuning and retrieval-based approaches for item localization.
- Side Camera Image Quality Tests — Define image quality metrics and build automated test suites for side-facing cameras.
- Location Toolbox Automation — Automate software tests for location-related modules.
- Item Location Evaluation Pipeline — Build pipelines for annotation, validation, and metric reporting.
Responsibilities
Depending on your assigned project, you may:
- Read relevant papers or internal docs and translate ideas into working prototypes.
- Prepare datasets, run baseline experiments, and track results in a reproducible way.
- Build or extend test automation for perception and localization software.
- Define and implement image quality or model evaluation metrics.
- Develop pipeline components for annotation, validation, and reporting.
- Write clear documentation and present progress to the team.
- Collaborate with engineers across localization, perception, and platform teams.
About You
Minimum Qualifications
- Currently enrolled in (or recently graduated from) a Computer Science, Engineering, Data Science, or related program.
- Solid programming fundamentals in Python (JavaScript a plus).
- Comfortable using Git, Linux shell, and collaborative development tools.
- Able to read technical material and implement solutions with mentor guidance.
- Clear written and verbal communication in English.
- Curious, self-motivated, and willing to ask questions.
Preferred Qualifications
- Coursework or project experience in machine learning, computer vision, or robotics.
- Familiarity with PyTorch or similar ML frameworks.
- Experience with test automation, CI pipelines, or QA tooling.
- Exposure to camera systems, image processing, or data pipelines.
- Prior internship, coursework project, or open-source contribution demonstrating initiative.
We do not expect production ML experience or advanced degrees. Strong fundamentals, learning ability, and interest in the problem space matter most.
What We Offer
- Direct mentorship from Spatial Intelligence engineers with weekly 1:1 check-ins and code reviews.
- Hands-on work on problems tied to live smart-cart technology.
- Flexible part-time schedule (3–4 days/week) suitable for students.
- Access to team learning resources and a collaborative, low-politics engineering culture.
Who Should Join Us
We are looking for motivated students and early-career builders who enjoy real hardware and real-world AI problems, learn quickly, communicate clearly, and are excited about retail automation and embodied AI. Every day we solve complex problems to create a shopping experience that feels effortless for users. If you want to grow as an engineer while contributing to technology deployed in stores worldwide, we'd love to hear from you.