Research Engineer

Job SummaryThe Alignment of Dynamical Cognitive Systems program seeks a Research Engineer to develop novel AI platforms addressing critical alignment challenges and practical LLM agents. Combining platform development with alignment research, this role requires expertise in agent-based systems and multi-objective optimization. You'll co-design and develop systems supporting safety research, internal tools, and community resources -- creating environments to shape multi-agent dynamics and implement cooperative AI architectures with robust alignment properties. You'll also build technical infrastructure to investigate experimental AI alignment and control approaches while collaborating with researchers to bring theoretical approaches into practice. This position offers the chance to develop foundational technologies essential for promoting safety as AI capabilities rapidly advance.
About CARMAThe Center for AI Risk Management & Alignment (CARMA) works to help society navigate the complex and potentially catastrophic risks arising from increasingly powerful AI systems. Our mission is specifically to lower the risks to humanity and the biosphere from transformative AI.We focus on grounding AI risk management in rigorous analysis, developing policy frameworks that squarely address AGI, advancing technical safety approaches, and fostering global perspectives on durable safety. Through these complementary approaches, CARMA aims to provide critical support to society for managing the outsized risks from advanced AI before they materialize.CARMA is a fiscally-sponsored project of Social & Environmental Entrepreneurs, Inc., a 501(c)(3) nonprofit public benefit corporation.
Responsibilities• Research and develop AI systems and platforms variously for: safety research, internal tooling, and safety community support• Design and implement architectures for agent execution environments and interaction platforms• Develop optimization algorithms for multi-objective and cooperative AI systems• Create mechanisms for conflict resolution and preference aggregation in multi-agent settings• Implement testing frameworks and example environments to validate theoretical approaches• Build middleware components that facilitate secure and efficient agent communication• Architect and develop reusable software components for AI alignment research• Document system architectures, APIs, and implementation details• Collaborate on technical publications and research presentations• Contribute to both practical implementations and theoretical research in AI alignment• Support the evaluation and refinement of research prototypes
Required Qualifications• MS or PhD in Computer Science, AI, or related field (or equivalent experience)• Experience with one or preferrably more of: dynamic plan recognition, activity recognition, dynamic multicriteria decision making, multi-agent systems, or AI builder platforms• Strong programming skills in both Python and Java• Demonstrated ability to relatively independently drive technical projects from concept to implementation• Familiarity with techniques for AI alignment, control, safety, or related research areas• Experience developing middleware, frameworks, or platforms• Breadth of experience in machine learning and AI systems architecture• Literate in semi-formal semantics • Excellent communication skills and technical documentation abilities • Publications (formal or informal) in AI alignment, AI safety, or related research areas• Familiarity with varied multi-objective optimization techniques• Experience with contextual pragmatics and/or constraint satisfaction problems• Developed novel multi-agent architectures• Experience with LLMs and integrating them into agent architectures• Experience with distributed systems and platform development• Knowledge of security and privacy methods for multi-party computation• Understanding of basic game theory and/or social choice theory concepts• Familiarity with metacognitive systems, metareasoning, or dynamical cognitive approaches• Demonstrable open source project contributions• Experience building systems that actively manage trade-offs between competing objectives