AI Jobs

This role involves designing and implementing advanced machine learning solutions with a focus on generative AI models, LLMs, and computer vision at scale. Required expertise includes 7+ years in data science and machine learning with proven experience in building production-grade AI systems.

Leads frontier AI research projects at Georgetown's Center for Security and Emerging Technology, coordinating research teams and briefing policymakers on AI and national security policy. Requires expertise in artificial intelligence, machine learning, data analysis, and the ability to design research plans, manage teams, and produce rigorous publications.

This role bridges cutting-edge AI research and practical application within Scale's Agent Capabilities & Environments team, focusing on autonomous agents that interact with diverse external environments including code repositories, GUI interfaces, and browsers. Required expertise includes machine learning research, agent systems, reinforcement learning, LLM capabilities, and benchmarking frameworks for evaluating model performance.

Lead the technical strategy and development of next-generation deep research agents for enterprise applications, translating cutting-edge AI research into production systems. This role combines hands-on machine learning engineering with strategic technical leadership, focusing on LLMs, agentic frameworks, and advanced knowledge retrieval systems.

Lead a team of research scientists and engineers to develop novel evaluation methodologies, metrics, and benchmarks for assessing large language model capabilities and limitations. Design cutting-edge LLM evaluation techniques and conduct research on effectiveness of existing evaluation approaches to advance generative AI development.

Lead the development of novel evaluation methodologies, metrics, and benchmarks to measure the capabilities and limitations of frontier large language models. Drive research on LLM evaluation effectiveness, design innovative benchmarks for instruction following and factuality, and define best practices in data-driven AI development.