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# Senior Machine Learning Engineer

- **Company:** Harnham Inc.
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $300,000.0
- **Contract:** Permanent contract
- **Skills:** 3D Scanning, 3D Computer Graphics Software, Artificial Intelligence, Architectural Patterns, Computer Vision, Computer Graphics, Distributed Computing Environment, Python (Programming Language), Machine Learning, Open Source Technology, NeRF (Neural Radiance Fields), Tensorflow, Computational Geometry, Neural Rendering, Pytorch, Delivery Pipeline, Generative AI, Gaussian, Information Technology, Variational Autoencoders, Machine Learning Operations, Stable Diffusion, Data Pipelines
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25304_e9db9d8ddb5db0c989f611ad361c8ce9

## About the Role

* Master's degree or higher in Computer Science, Machine Learning, Computer Vision, or related field
 * 3+ years of experience in machine learning, computer vision, or computer graphics
 * Strong Python skills and experience with frameworks such as PyTorch or TensorFlow
 * Experience with generative models (diffusion, GANs, VAEs) and modern architectures (Transformers, ViTs, etc.)
 * Solid understanding of 3D graphics, computational geometry, or related areas
 * Experience working in cross-functional teams and communicating technical concepts clearly, * Experience with 3D reconstruction, tracking, or geometry analysis
 * Familiarity with neural rendering techniques such as NeRF or Gaussian Splatting
 * Experience scaling ML models and optimizing training pipelines
 * Background in distributed training, data pipelines, and experiment tracking
 * Publications or contributions to research or open-source projects

## Description

* Design, train, and deploy ML models for high-quality image, video, and 3D asset generation
 * Work across computer vision and graphics to generate meshes, textures, and motion
 * Build and optimize generative models such as diffusion, GANs, and transformer-based architectures
 * Integrate ML systems into user-facing creative tools and applications
 * Collaborate with product, engineering, and creative teams to ship AI-powered features
 * Manage ML infrastructure including datasets, experiments, and deployment pipelines
 * Stay up to date with emerging research and apply new techniques to production systems