Machine Learning Evaluation Engineer
Are you passionate about making ML models more transparent? Explaining when and how a ML model could fail with tests and statistics? Are you passionate about being a gate keeper for ML models that might impact millions of people? Are you passionate about the latest development in 3D Computer Vision and Graphics?
We are seeking a Machine Learning (ML) Evalution Engineer specializing in 3D generation models to evaluate, optimize, and interpret machine learning models used for 3D content creation. In this role, you will analyze model performance, identify weaknesses, and improve robustness, efficiency, and realism in generated 3D assets. You will work closely with researchers, engineers, and product teams to ensure high-quality outputs in areas such as 3D reconstruction, generative modeling, and rendering.
If you are passionate about 3D generative AI and pushing the boundaries of AI-driven 3D content creation, we would love to hear from you!
- Evaluate and Optimize 3D Generative Models: Assess and fine-tune various 3D generative models, including NeRF, GANs, diffusion models, and implicit neural representations, to enhance their performance.
- Develop Advanced Quality Metrics: Design and implement sophisticated quality metrics tailored for evaluating 3D models, encompassing both visual and geometric aspects (e.g., Chamfer Distance, IoU, FID, PSNR, SSIM, and beyond).
- Model Analysis and Robustness Improvement: Utilize techniques like SHAP, LIME, and adversarial testing to identify model weaknesses, biases, and generalization issues. Propose and implement solutions to improve robustness and scalability.
- Collaborative Cross-Functional Teamwork: Work closely with computer vision researchers, graphics engineers, and ML engineers to develop and integrate cutting-edge 3D generation technologies.
- MLOps and Model Monitoring: Implement MLOps practices and utilize model tracking tools (e.g., MLflow, Weights & Biases) to monitor and continuously improve model performance.
- Bachelor or above degree in Computer Science, Electrical Engineering, or a related field.
- Strong proficiency in Python and experience with ML frameworks such as TensorFlow and PyTorch.
- Solid understanding of computer vision principles and extensive experience with 3D deep learning techniques, including NeRF, PointNet, MeshCNN, and Diffusion Models.
- Hands-on experience with image/video/3D generative ML models and a proven track record of working with 3D data (point clouds, meshes, voxels, signed distance functions).
- Familiarity with advanced evaluation metrics for both 2D and 3D generative models.
- Proficiency in 3D data processing and rendering frameworks such as Blender API, Unreal Engine, Open3D, Kaolin, or Minkowski Engine.
- Experience with real-time rendering and physics-based simulations.