Visual Generation & Multimodal Evaluation Machine Learning Engineer Graduate (AML-Ark-US) - 2027 Start

What you'll do

• Build evaluation systems for image and video models and agents, covering generation quality, instruction following, multimodal understanding, and safety.
• Develop automated metrics, model-based evaluators, and reproducible human evaluation protocols; design video generation and debugging agents.
• Build large-scale image and video data pipelines and use evaluation findings to improve models and products.

What they ask for

• Completing or recently completed a Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or a related field.
• Solid foundation in deep learning and computer vision, including generative modeling fundamentals.
• Practical experience in at least one of visual generation, multimodal LLMs, video understanding, or visual quality assessment.
• Strong Python skills and proficiency with PyTorch or an equivalent framework, or a multimodal evaluation framework; demonstrated research or engineering ability through publications, substantial projects, internships, or open-source work.

Nice to have

• Publications at top-tier vision or ML venues, such as NeurIPS, ICML, CVPR, ICCV, or ECCV.
• Hands-on experience with diffusion-based visual generation models and post-training; familiarity with visual generation benchmarks or evaluation frameworks; or experience with creative-workflow agent frameworks or large-scale video data infrastructure.

Tools

Python, PyTorch, diffusion-based models

AI focus

Builds image and video model evaluation systems and video generation/debugging agents; focuses on generative modeling, multimodal understanding, safety, and model improvement.