Research Engineer - NeRF & GenAI

About Netropy AI

Netropy AI is a startup pioneering Physical AI and high-fidelity simulation solutions for industries such as autonomous driving and robotics. We leverage video-based 3D reconstruction, advanced computer vision, and generative technologies to bridge the gap between the virtual and physical worlds.

About the Role

We are looking for aResearch Engineer to join our team and push the boundaries of Physical AI. In this role, you will combine cutting-edge research in generative AI and foundation models with image and 3D reconstruction. You will play a pivotal role in developing the next generation of simulation technology.

Key Responsibilities

• Innovate & Ideate: Generate, propose, and evaluate novel research ideas to drive the continuous evolution of our AI technology stack.
• Advanced Development: Develop and implement state-of-the-art 3D/4D reconstruction technologies, including Gaussian Splatting, Vision Generative Models, and Geometric Foundation Models.
• Data Engineering: Manage the lifecycle of large-scale datasets, including preprocessing and curation, to support robust model training and evaluation.
• Product Integration: Optimize research prototypes and integrate developed AI models into our core products, ensuring seamless performance and scalability.

Qualifications

Required (Must-Have)

To be considered for this role, applicants must meet the following criteria:

• Experience: 2+ years of research experience in Computer Vision, Computer Graphics, or related fields (Academic or Industrial).
• Generative AI Expertise: A deep understanding of Vision Generative and Foundation Models (e.g., Diffusion models, Transformers).
• Technical Foundation: Strong grasp of traditional computer vision and fundamental mathematics (Linear Algebra, Calculus, Statistics, and Probability).
• Deep Learning Proficiency: Extensive knowledge of Deep Learning literature with the ability to rapidly comprehend and implement new technologies.
• Mindset: Strong critical thinking skills, a hands-on approach to problem-solving, and the ability to learn quickly in a fast-paced environment.
• Communication: Professional proficiency in English for technical collaboration.

Preferred (Nice-to-Have)

• Specialized Research: Experience researching Vision Diffusion or Flow Matching models.
• Large-Scale Training: Hands-on experience training or fine-tuning Large Vision Models (LVMs) on multi-GPU setups.
• Publication Record: A track record of publishing research at top-tier conferences such as CVPR, SIGGRAPH, ICCV, NeurIPS, or ECCV.