Machine Learning Engineer Intern at Genies in LA Metro Area

Summary: Genies is looking for a ML Engineering Intern to join their R&D group this summer. The intern will work closely with a talented team to explore new concepts and technologies aimed at empowering users to develop their own avatar ecosystems. Responsibilities: - Develop tools and workflows to support training and experimentation with multi-view generation models - Explore techniques to improve multi-view consistency across generated views to ensure coherent geometry and texture reconstruction - Prototype and evaluate models that improve the integration between 2D generation and downstream 3D reconstruction pipelines - Benchmark model outputs and analyze results to identify failure cases and opportunities for improvement - Collaborate with engineers and researchers across graphics, ML, and avatar systems, and present findings and recommendations Required Qualifications: - Currently enrolled in or a recent graduate of a PhD or Master degree program in Computer Science, Artificial Intelligence, Machine Learning, or a related field - Solid understanding of machine learning concepts and recent computer vision - Familiarity with programming languages such as Python and experience with machine learning libraries (e.g., PyTorch, TensorFlow, scikit-learn, Hugging Face) - Ability to conduct research and prototype solutions based on academic literature - Strong problem-solving skills and the ability to work independently as well as in a team environment - Excellent communication skills, with the ability to present findings clearly and effectively Preferred Qualifications: - Experience with multi-view generation, 3D reconstruction, or view-consistent generative models - Familiarity with diffusion models or generative AI systems - Experience working with 3D data such as meshes, normals, or UV textures - Knowledge of avatar generation pipelines or asset retrieval systems Required Skills: Machine Learning, Computer Vision, Python, PyTorch, TensorFlow, scikit-learn, Hugging Face, Research, Prototyping based on Academic Literature