Lead Data Scientist - Autonomous Goal Management

Accountabilities:

• • 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 environment, maintain strong collaboration across teams, and communicate complex technical concepts clearly.

  Benefits:

  • Base salary range of $142,300–$195,700 per year, depending on location, experience, skills, education, certifications, and other job-related factors.
  • Eligibility for a performance-based bonus incentive plan.
  • Fully remote work within the United States.
  • Medical, dental, and vision insurance.
  • 401(k) retirement savings plan.
  • Paid time off, company holidays, and personal holidays.
  • Paid parental and caregiver leave.
  • Short-term and long-term disability coverage.
  • Life insurance.
  • Whole-person wellness and healthcare support programs.
  • 40-hour standard work week.
  • Occasional travel to company offices for training or meetings may be required.
  • Remote employees receive support for maintaining a suitable, dedicated workspace and reliable internet connection.