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1 week ago
Develop a next-generation evaluation system by leveraging automatic evaluations.
The ideal candidate will engineer sophisticated AI agents that can automatically assess the quality and human alignment of generative design models. This high-impact role focuses on building the practical systems that make cutting-edge research effective, to provide a rapid feedback loop that guides the future of design generation.
This includes:
- Agentic Evaluation Systems: Engineer autonomous AI agents that use Multimodal Large Language Models (MLLMs) to evaluate the quality, relevance, and human alignment of generated designs.
- Inference-Time Alignment: Master techniques that improve model outputs without full retraining, but by inference-based methods including prompt engineering, in-context learning and Retrieval-Augmented Generation (RAG).
- Model Benchmarking & Analysis: Build a rigorous framework to systematically benchmark internal and external quality understanding models, delivering clear, data-driven insights on human alignment.
The successful candidate will be responsible for:
- Designing, building, and optimizing the infrastructure for an 'MLLM-as-a-Judge' evaluation system for scalable, automated feedback.
- Implementing and experimenting with inference-time alignment techniques (Prompt Engineering, RAG, ICL) to directly improve model output quality.
- Establishing and managing a comprehensive benchmarking process to compare various foundation models on design-centric tasks.
- Analyzing evaluation data to identify model failure modes and providing actionable recommendations to the research team.
- Collaborating with research scientists and ML engineers to integrate the agentic judge system into the model development lifecycle.
- Translating the latest research in LLM evaluation and agentic AI into practical, production-ready engineering solutions.
The ideal candidate should possess:
- A strong understanding of generative AI models (e.g., Diffusion Models, GANs, Transformers) and their architectures, with practical experience that informs robust evaluation strategies.
- Excellent skills in creating data-driven evaluation methodologies, turning user analytics into clear, actionable insights.
- Experience in managing or optimizing large-scale distributed model training across hundreds of GPUs.
- A solid understanding of machine learning, having worked with PyTorch and knowing how to optimize such codes for speed.
- Disciplined coding practices, with experience in code reviews and pull requests.
- Experience working in cloud environments, ideally AWS.
The following qualifications are desirable:
- Familiarity with evaluation libraries and frameworks.
- Experience building or working with agentic AI systems or multi-agent coordination.
- Knowledge of data visualization tools to communicate findings effectively.
- A background or interest in human-computer interaction, design principles, or AI ethics.
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