Machine Learning Engineer - 3D

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.

Originally posted on Himalayas