Research Scientist - Autonomous Systems

About the role

About Evolver

Evolver is an AI technology company transforming professional services. We combine deep domain expertise, advanced AI, and continuous applied learning to automate complex enterprise workflows while keeping people involved where judgment matters. Our goal is to help organizations operate more efficiently, accurately, and intelligently.

The Role

Evolver 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

- state estimation

- optimization or optimal control

- stochastic systems

- sequential decision-making

- planning under uncertainty

- reinforcement learning

- autonomous systems

- Strong Python or equivalent scientific computing skills.

- Ability to translate mathematical concepts into computational methods and working prototypes.