Back to search
Description
Within the DAQ team, our core mission is to evaluate and elevate advanced visual technologies. As a key member of this group, you will lead the benchmarking and integration of state-of-the-art models for image and video understanding. Rather than focusing on core model training, you will apply your deep CV and ML expertise to rigorously test models in applied settings, uncover edge-case failure modes, and architect advanced agentic systems. If you are passionate about AI safety, robust evaluation, and building autonomous multi-modal workflows that bridge experimentation with production, we’d love to hear from you.
Minimum Qualifications
MS and a minimum of 3 years relevant industry experience 3+ years of applied experience in Machine Learning, Computer Vision, or AI System Evaluation Solid ML Foundation: Deep understanding of core Machine Learning principles, including probability, statistics, data distributions, and model bias/variance. You can apply statistical rigor to ensure evaluation metrics are meaningful and reliable. Computer Vision Expertise: Deep theoretical and practical understanding of Computer Vision (CV) and Vision-Language Models (VLMs). You must understand how Vision Transformers (ViTs), spatial-temporal modeling, and image/video processing work under the hood to effectively evaluate them. Advanced Evaluation Skills: Proven track record of defining robust metrics/KPIs and designing rigorous evaluation frameworks for generative AI or foundation models. Deep experience with custom benchmark creation, automated regression testing, LLM/VLM-as-a-judge methodologies, and human-in-the-loop evaluation. Agentic Systems: Experience building and evaluating LLM/VLM-powered agents, including tool use, multi-step reasoning, planning, and memory management workflows. Failure Analysis: Strong intuition for probing ML models to discover edge cases, hallucinations, and performance bottlenecks in constrained environments. Be able to translate findings into actionable improvement recommendations. Engineering Excellence: Strong proficiency in Python and experience with deep learning frameworks (PyTorch) for running inference, extracting embeddings, and building scalable evaluation pipelines.
Preferred Qualifications
Demonstrated ability to lead technical evaluation strategies end-to-end, drive architectural decisions for testing infrastructure, and mentor engineers. Strong foundation in statistics, including hypothesis testing, confidence intervals, and experimental design Knowledge of reinforcement learning, planning, or decision-making systems Experience evaluating multi-modal or multi-agent systems Prior work on AI reliability, safety, or benchmarking
Learn more about this Employer on their Career Site
