Formlabs
Algorithms Software Intern (Fall 2026)
Somerville, MA$48.1k–$58.5kinternshipinternAdded today
About this role
Formlabs seeks an Algorithms Software Intern to develop high-performance C++ solutions for 3D printing software, focusing on computational geometry, laser path planning, and physics modeling. You'll work on desktop applications bridging digital designs to physical output, optimizing algorithms and contributing to cutting-edge product features.
What you'll do
- Develop performant algorithms in modern C++ for laser path planning, physics modeling, and computational geometry
- Optimize existing code for performance and efficiency in resource-constrained environments
- Contribute to cross-platform desktop applications using C++17 and Qt
- Design and implement well-tested, maintainable code with focus on reusability
- Collaborate in an agile environment on new product development
- Research and implement state-of-the-art solutions in image processing and geometry algorithms
What they're looking for
- C++ (C++17 preferred)
- Computational geometry and 3D algorithms
- Performance optimization and low-level programming
- Image processing techniques
- Qt framework for desktop applications
- Physics modeling or simulation
- Parallel computing concepts
- Mathematical problem-solving
Benefits
- Flexible time-off policy
- Bi-weekly salaried pay
- On-site parking and commuter benefits
- Catered lunches three times per week with snacks and beverages
- Cohort-based intern social and professional development programs
- Unlimited 3D printing access
Opens the official application on the employer’s site. No login required.
Formlabs
Formlabs builds advanced 3D printing hardware and systems, with particular focus on dental and professional applications. The company is hiring for engineering roles including robotic systems integration, mechanical design, and reliability testing to develop and improve their 3D printing technology.
- Website
- formlabs.com
Likely interview questions
- Walk us through a complex algorithm you've optimized for performance—what techniques did you use and what were the results?
- How would you approach learning a completely unfamiliar domain in computational geometry or physics modeling?