Visual Generation & Multimodal Evaluation Machine Learning Engineer Graduate

About the Role

The Applied Machine Learning Ark team develops and operates Large Language Model (LLM) service platforms offering Model-as-a-Service (MaaS) solutions. The US team focuses on the design, development, and operation of MaaS solutions across the US and international markets outside mainland China. The team builds full-stack, end-to-end solutions covering text and multimodal LLM algorithms, LLM training/fine-tuning/inference frameworks, prompt engineering, model alignment, and intelligent agent systems. Beyond model serving, the team operates large-scale log analytics pipelines for text, multimodal models, and agent systems to extract insights for improvement and optimization.

Key Responsibilities

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

Requirements & Tech Stack

• Python skills.
• Proficiency with PyTorch or an equivalent framework, or a multimodal evaluation framework.
• Deep learning and computer vision foundation, including generative modeling fundamentals.
• Practical experience in at least one of: visual generation, multimodal LLMs, video understanding, or visual quality assessment.

Qualifications

• Currently completing or recently completed a Bachelor's/Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or a related field.
• 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).
• 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.