Machine Learning Engineer Intern

Genies is looking for a ML Engineering Intern to join our R&D group this summer. Based either in our Los Angeles or San Francisco offices (Hybrid), you will work closely with a dedicated and talented team of technical artists, engineers and artists. Together, you will explore new concepts and technologies to further Genies' mission of empowering users to develop their own avatar ecosystems. We're looking for someone who is passionate about creating high-quality visuals and has the technical foundation to help us build the next wave of digital identity.

What You’ll be Doing:

* Research and prototype methods to improve 3D avatar texture quality, and improving texture realism..

* Develop techniques to separate lighting from albedo textures and improve the consistency of generated textures across different lighting conditions.

* Train, fine-tune, or adapt machine learning models to enhance texture generation and improve visual fidelity.

* Evaluate results using both quantitative metrics and visual inspection, and benchmark improvements in texture quality.

* Collaborate with engineers and researchers across graphics, ML, and avatar systems, and present experiment findings and recommendations.

What You Should Have:

* 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.

Bonus Skills (Nice-to-Have)

* Experience with 3D data, rendering, or texture processing (e.g., UV textures, albedo maps, PBR pipelines).

* Familiarity with image-to-image models, diffusion models, or generative models for texture or image enhancement.

* Experience with image decomposition tasks such as intrinsic image decomposition (separating lighting and reflectance).

* Knowledge of 3D generation or avatar pipelines.