Intern in Machine Learning Engineering

About

We are looking for a highly motivated intern to join our R&D team at Align Technology Zurich. You should be available for 15h/week on-site, and you will be compensated with 30CHF/hour. To be considered for this role, you must be enrolled in a full-time degree program.

Key Responsibilities

• Evaluate and analyze the output quality of 3D generative models to inform design decisions through systematic testing and benchmarking.
• Design and implement automated evaluation pipelines for 3D generative networks and their output meshes.
• Develop metrics and tests to assess quality, robustness, and failure cases of generated 3D geometry.
• Write automated tests to validate code correctness and model behavior.
• Work on 3D geometry processing and constraint optimization to define and verify mesh quality criteria.
• Analyze model outputs, identify limitations, and propose improvements to the team.
• Collaborate closely with the Advanced Technology Development team in Zurich.
• Get insights into a modern, fast-growing Research & Development department of an international corporation.

Skills, Knowledge & Expertise

• Enrollment in a degree program in computer science, mathematics, engineering or a related field.
• Good Python programming skills and basic software engineering knowledge.
• Familiarity with PyTorch or similar deep learning frameworks.
• Understanding of 3D geometry processing concepts.
• Basic understanding of common generative network architectures (e.g., AE, VAE, GAN, diffusion models).
• Solid grounding in linear algebra, geometry, and optimization.
• Attention to detail and high standards for quality.
• Good communication skills in English.
• Good organizational skills.

Nice to Have

• Experience with 3D geometry processing libraries (e.g., trimesh, open3d, pymeshlab, libigl).
• Experience with constraint optimization.
• Experience writing automated tests or building CI/CD pipelines.
• Familiarity with data processing libraries such as NumPy, SciPy, scikit-learn.
• Experience training or fine-tuning ML models.
• Knowledge of evaluation methodologies for generative models.