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

What you'll do

• Build image and video model and agent evaluation systems for generation quality, instruction following, multimodal understanding, and safety.
• Develop automated metrics, model-based evaluators, reproducible human evaluation protocols, and video generation/debugging agents.
• Build large-scale image and video data pipelines and use evaluation findings to drive model and product improvements.

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 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.
• Experience with diffusion-based visual generation and post-training, visual generation benchmarks or evaluation frameworks, agent frameworks for creative workflows, or large-scale video data infrastructure.

Tools

Python, PyTorch

AI focus

Evaluation and improvement of image and video generation models and multimodal agents; automated and human evaluation, and agents orchestrating creative workflows.