Lead Data Scientist - Autonomous Goal Management

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead Data Scientist - Autonomous Goal Management based in United States. This is a high-impact opportunity to shape the next generation of autonomous AI systems in a large-scale healthcare environment. You will lead research and engineering efforts focused on agents that can independently set, decompose, adapt, and execute complex goals. The role combines advanced machine learning research with hands-on development of reliable, production-ready AI systems. You will explore planning, memory, tool use, feedback loops, and alignment mechanisms for long-horizon autonomous behavior. Your work will help make AI agents more robust, interpretable, safe, and effective in dynamic real-world settings. You will collaborate with multidisciplinary teams to translate emerging research into reusable technologies and operational capabilities. This is a fully remote U.S. role offering the opportunity to influence AI innovation at significant scale. Architect goal-setting and goal-decomposition mechanisms that enable autonomous agents to operate effectively in uncertain, open-ended environments. Design and implement dynamic planning approaches, including hierarchical planning, curriculum learning, scratchpad methods, and self-refinement loops. Develop memory, tool-use, and feedback-loop capabilities that support multi-step, self-directed agent behavior. Build evaluation frameworks that measure alignment with human intent, consistency, progress, and performance against long-horizon objectives. Prototype autonomous agents capable of interacting with APIs, MCP servers, search engines, databases, and other real-world systems while maintaining safe and efficient behavior. Investigate methods for identifying and mitigating goal misalignment, looping behavior, undesirable emergent strategies, and other autonomy risks. Collaborate across AI, safety, alignment, and engineering teams to integrate goal management capabilities with broader reliability and risk-management mechanisms. Lead the development of AI/ML systems and contribute to research that can transition into scalable, production-ready solutions. Apply data analysis, experimentation, and evaluation techniques to continuously improve agent performance and reliability. Requirements: Master's degree in Computer Science, Data Science, Machine Learning, or a related discipline, combined with 4+ years of experience in research, ML engineering, or applied research focused on production-ready AI solutions. 2+ years of experience leading the development of AI/ML systems. Strong proficiency in Python, SQL, and data analysis or data-mining tools. Hands-on experience with machine learning frameworks and agent-development technologies such as PyTorch, JAX, LangChain, LangGraph, or AutoGen. Experience designing or implementing high-performance, large-scale machine learning systems. Strong understanding of language modeling and transformer-based architectures. Experience with symbolic planning, causal reasoning, model-based reinforcement learning, or related approaches to autonomous decision-making. Experience with large-scale ETL and data-processing pipelines. Demonstrated ability to research, prototype, evaluate, and deliver sophisticated AI systems. Strong analytical and problem-solving skills, with the ability to investigate complex technical challenges and translate research into practical solutions. Preferred: Ph.D. in Computer Science, Data Science, Machine Learning, or a related field. Preferred: Experience deploying autonomous or semi-autonomous agents in production or simulation environments. Preferred: Experience with LLM-based agents using scratchpad/self-reflection techniques or hierarchical task decomposition. Preferred: Understanding of autonomy risk mitigation strategies, including bounded rationality and off-switch protocols. Ability to work effectively in a remote