Visual Generation & Multimodal Evaluation Researcher Graduate

About the Role

The Applied Machine Learning Ark team at ByteDance combines system engineering and machine learning to develop and operate Large Language Model (LLM) service platforms. The US team focuses on designing, developing, and operating MaaS solutions for both domestic and international markets. The team is building full-stack, end-to-end solutions covering text and multimodal LLM algorithms, training/fine-tuning/inference frameworks, prompt engineering, model alignment, and intelligent agent systems.

Key Responsibilities

• Build evaluation systems for image and video models/agents, covering generation quality, instruction following, multimodal understanding, and safety.
• Develop automated metrics and model-based evaluators, and design reproducible human evaluation protocols.
• Design and develop video generation/debugging agents that orchestrate multi-step creative workflows.
• Build large-scale image and video data pipelines, and translate evaluation findings into model and product improvements.

Requirements & Tech Stack

• Strong Python skills.
• Proficiency with PyTorch or an equivalent framework, or a multimodal evaluation framework.

Qualifications

• Individuals who are completing or have recently completed a PhD'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.
• Demonstrated research or engineering ability through publications, substantial projects, internships, or open-source work.

Preferred Qualifications

• Publications at top-tier vision or ML venues, e.g., NeurIPS, ICML, CVPR, ICCV, ECCV, etc.
• Hands-on experience with modern visual generation stacks, including diffusion-based models and their post-training.
• Familiarity with visual generation benchmarks, or experience building evaluation frameworks.
• Experience applying agent frameworks to creative workflows, or working with large-scale video data infrastructure.