Autonomous Systems Research Scientist – State Estimation & Planning

Evolver; Palo Alto (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Evolver is seeking a Research Scientist specializing in autonomous systems, control, and optimization to develop methods that enable robust decision-making under uncertainty. You will translate mathematical theory into practical algorithms and prototypes, collaborating across research, engineering, and product teams.

Applicants with strong mathematical maturity, publication record, and a PhD or thesis-based Masters are encouraged, including exceptional recent graduates.

Qualifications

• Ph.D. or thesis-based Master’s in a quantitative field with thesis title and contributions described in application.
• Strong foundation in mathematical modeling and systems thinking.
• Research experience in dynamical systems/ control, optimization, stochastic systems, RL, or autonomous systems.

Responsibilities

• Develop methods for state and world modeling, estimation, planning, control, and sequential decision-making.
• Design approaches for autonomous systems 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 to complex AI systems.
• Develop methods to evaluate 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 methodologies into real systems.

Skills

Autonomous systems

Control theory

Mathematical modeling

Optimization

Uncertainty decision-making

Python

Research communication

Education

Ph.D. or thesis-based Master in Electrical Engineering/Control/Robotics/Applied Math/OR/CS

Tools

Python

MATLAB

Job description

Evolver is seeking a Research Scientist specializing in autonomous systems, control, and optimization to develop methods that enable robust decision-making under uncertainty. You will translate mathematical theory into practical algorithms and prototypes, collaborating across research, engineering, and product teams.

Applicants with strong mathematical maturity, publication record, and a PhD or thesis-based Masters are encouraged, including exceptional recent graduates.