Research Scientist - Autonomous Systems

What you'll do

• Develop methods for state and world modeling, estimation, planning, control, and sequential decision-making.
• Design and evaluate adaptive autonomous systems that operate under uncertainty and incomplete information.
• Translate research into algorithms, prototypes, benchmarks, evaluation methods, and product architectures, collaborating with research, engineering, and product teams.

What they ask for

• Ph.D. or thesis-based Master's degree in a quantitative field listed in the posting or a related field.
• Strong foundation in mathematical modeling and systems thinking, plus research experience in one or more relevant areas such as control, state estimation, optimization, or reinforcement learning.
• Strong Python or equivalent scientific computing skills.
• Ability to translate mathematical concepts into computational methods and working prototypes.

Nice to have

• Research experience in areas such as model predictive control, POMDPs, or formal verification; or experience with complex autonomous systems.
• Strong publication record, research impact, production research transfer, or familiarity with modern machine learning, foundation models, or agentic AI systems.

Tools

Python, reinforcement learning, model predictive control, POMDPs, Bayesian estimation, system identification, formal verification, foundation models

AI focus

Develop methodologies for AI systems that maintain internal state, reason under uncertainty, plan and act, evaluate outcomes, and adapt over time.