Lead Data Scientist - Autonomous Goal Management
New
J
JobgetherHealthcare AI
Fully remote U.S. roleFull-TimeLead
SalaryBase salary range of $142,300–$195,700 per year
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Job Details
- Experience
- 4+ years of experience in research, ML engineering, or applied research
- Required Skills
- PythonSQLData AnalysisETLMachine LearningPyTorchLangChain
Requirements
- Master's degree in Computer Science, Data Science, Machine Learning, or a related discipline.
- 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.
- Ph.D. in Computer Science, Data Science, or Machine Learning preferred.
Responsibilities
- 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.
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