The RoleEvolver is looking for a Research Scientist with a strong background in autonomous systems, control, mathematical system theory, optimization, or decision-making under uncertainty.
You will help develop the methodologies underlying intelligent systems that perceive changing environments, maintain and update internal state, reason under uncertainty, plan and act, observe outcomes, and adapt over time.
We welcome applications from exceptional recent graduates as well as experienced researchers. We care more about depth of thinking, mathematical maturity, and research ability than years of industry experience.
Experience in robotics or other physical autonomous systems is valuable, but this is not primarily a hardware or embedded-systems role.
What You'll Do - Develop methods for state and world modeling, state estimation, planning, control, and sequential decision-making.
- Design approaches for autonomous systems operating under uncertainty and incomplete information.
- Develop adaptive planning and feedback mechanisms that update decisions as new information becomes available.
- Apply optimization, control, probabilistic reasoning, reinforcement learning, or related methods to complex AI systems.
- Develop methods for evaluating the robustness, reliability, and behavior of autonomous systems.
- Translate research into algorithms, prototypes, benchmarks, evaluation methods, and product architectures.
- Collaborate with research, engineering, and product teams to bring new methodologies into real systems.
Minimum Qualifications - Ph.D. or thesis-based Master's degree in Electrical Engineering, Control, Robotics, Applied Mathematics, Operations Research, Computer Science, Systems Engineering, or a related quantitative field (Note: Please include the title of your thesis in your application and a brief summary of the problem, methodology, and your specific contribution).
- Strong foundation in mathematical modeling and systems thinking.
- Research experience in one or more of:
- dynamical systems and control
- optimization or optimal control
- sequential decision-making
- planning under uncertainty
- Strong Python or equivalent scientific computing skills.
- Ability to translate mathematical concepts into computational methods and working prototypes.
There is no minimum number of years of industry experience. Strong candidates may demonstrate their capabilities through a thesis, publications, research projects, internships, open-source work, or relevant industry experience.
Preferred Qualifications - Research experience in areas such as model predictive control, stochastic control, POMDPs, Bayesian estimation, system identification, hybrid systems, multi-agent systems, or formal verification.
- Experience with robotics, autonomous vehicles, aerospace, industrial automation, or other complex autonomous systems.
- Strong publication record or demonstrated research impact.
- Experience transferring research into production or real-world systems.
- Familiarity with modern machine learning, foundation models, or agentic AI systems.
Deep prior experience with LLMs, RAG, prompt engineering, or specific agent frameworks is not required.
Application In your cover letter, please include the title of your thesis and a brief summary of the problem, methodology, and your specific contribution.
