Looking for a Machine Learning Engineer (3D Foundation Model Specialist) to design and fine-tune the AI models that generate and manipulate 3D assets on the fly. You’ll be at the core of a multi-modal, low-latency system that understands what users want and brings it to life through generative AI and cloud rendering. Responsibilities Design, fine-tune, or train 3D generative models (e.g. NeRFs, Gaussian Splatting, 3D diffusion, or transformers) for asset creation and manipulation. Build robust data pipelines to manage large 3D datasets: meshes, textures, point clouds, animations. Optimize model inference for real-time rendering — targeting seamless integration with AR/VR pipelines. Collaborate closely with cloud infrastructure and graphics engineers to deploy models on GPU-powered cloud environments and stream results to users in milliseconds. Stay on the frontier of 3D AI research: monitor papers, benchmark SOTA models, and integrate cutting-edge techniques into production. Ideal Candidate Strong experience with 3D AI and computer vision (point clouds, meshes, NeRFs, diffusion models, or similar). Proficient in Python, PyTorch or TensorFlow, and familiar with libraries like Open3D, Kaolin, or NVIDIA Omniverse. Experience training large models on cloud platforms (AWS, GCP, Azure) using GPU clusters. Passionate about real-time systems and immersive technology (VR/AR/XR). Bonus: Experience with integrating AI models into real-time engines (Unity, Unreal, WebGL/WebGPU). What We Value Comfortable navigating ambiguity and working independently. Action-oriented with a practical approach to solving complex problems. Strong ownership mentality and proven delivery on high-impact projects. Clear communicator with strong collaboration skills, especially with technical teams. Experience building from the ground up in fast-moving startup environments. Genuine enthusiasm for accelerating ML research and deployment in creative space. Bonus Skills (Nice-to-Have) Experience with ML pipelines involving video, image, or 3D data. Familiarity with distributed compute frameworks (e.g., Ray) or orchestration tools (e.g., Flyte). Familiarity with game engines (Unreal or Unity) Knowledge of vector databases and similarity search (e.g., LanceDB). Prior work in AI/ML research settings or startups. Contributions to open-source ML/data infrastructure projects. Experience designing tools directly for researchers or technical users.