Senior Machine Learning Engineer

Role Overview

Join Genies as a Senior Machine Learning Engineer to design, develop, and deploy machine learning models for the company’s avatar creation ecosystem. You’ll collaborate with engineering and creative teams to integrate AI/ML capabilities into product experiences.

Key Missions / Responsibilities

• Collaborate with R&D and application teams to integrate AI/ML solutions into the avatar creation ecosystem.
• Apply 3D computer vision and computer graphics techniques to build ML models.
• Design, train, fine-tune, accelerate, optimize, and deploy ML models and technical frameworks.
• Work deeply with computer graphics pipelines, computational geometry, and 3D principles.
• Manage ML framework resources and infrastructure, including:
  • Dataset management
  • Client/cloud compute options
  • Experiment tracking
  • Model deployment techniques

Requirements

• 3+ years of professional industry or research experience in machine learning, computer vision, or computer graphics.
• Strong proficiency in Python and extensive experience with AI/DL frameworks such as PyTorch or TensorFlow.
• Master’s degree or higher in Computer Science, Computer Graphics, or related fields with strong ML/AI focus.
• Ability to communicate complex ideas to non-technical audiences.
• Strong teamwork skills working across cross-functional teams and with stakeholders.

Nice to Have

• Advanced model architectures: Transformers, Vision Transformers (ViT), Diffusion Transformers (DiT), Mixture of Experts (MoE).
• Experience with multimodal training for 3D vision/graphics.
• Background in computer graphics, computational geometry, and 3D.
• Generative models: diffusion, GANs, VAEs, flow-based.
• Experience in AI-driven content creation (games/animation), e.g. 3D reconstruction, segmentation/tagging, avatar textures, body/object tracking, avatar behavioral systems.
• Neural rendering techniques: NeRF or Gaussian Splatting (scene representation learning, novel view synthesis).
• ML infrastructure experience: distributed training, data pipelines, model versioning, experiment tracking.
• Experience training/scaling large models and deploying to production (compute optimization).
• Publication track record (e.g., SIGGRAPH, CVPR, NeurIPS, ECCV, ICCV, ICML).
• Contributions to popular open-source ML projects.