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.
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.
About Evolver
Evolver is building intelligent enterprise systems that go beyond generating answers. Our systems must understand evolving situations, reason under uncertainty, make decisions, act, evaluate outcomes, and continuously improve.
We are looking for researchers who want to bring the foundations of systems, control, autonomy, and decision science into the next generation of AI.
