Evolver
Research Scientist - Autonomous Systems
Palo Alto, California
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About 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.
- 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.
Responsibilities
- 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.
- Develop methods for evaluating the robustness, reliability, and behavior of autonomous systems.
- Collaborate with research, engineering, and product teams to bring new methodologies into real systems.
- 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.
Requirements
- Research experience in one or more of:
- Ph.D. or thesis-based Master's degree in Electrical Engineering, Control, Robotics, Applied Mathematics, Operations Research,
Skills
- 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. </spa
Benefits
- Apply optimization, control, probabilistic reasoning, reinforcement learning, or related methods to complex AI systems.
- We combine deep domain expertise, advanced AI, and continuous applied learning to automate complex enterprise workflows while keeping people involved where judgment matters.
This listing is sourced directly from Evolver's careers page and normalized into a canonical job model.