Expert Optimal Control Scientist

About the Mission

Control is one of the decisive levers for how well this platform performs. The way a qubit is driven, stabilized, and read out determines how close the hardware gets to its theoretical limits. That is where this role sits.

About the Role

As an Expert Optimal Control Scientist, you will design and optimize the control strategies that maximize gate fidelity, state preparation, and measurement on the cat-qubit platform - operating point selection, pulse shaping, feedback stabilization, and quantum error correction optimization, among others.

About You

You'll report to the Team Lead, Control, and work closely with Chip Design, Performance Optimization, Calibration, and QEC to turn control-theoretic methods into measurable platform improvements - from simulation to deployment and iteration on real cat-qubit chips.

Responsibilities

Design and optimize quantum control strategies

• Develop pulse-shaping and feedback stabilization schemes to maximize gate fidelity
• Apply control and stabilization methods to the cat-qubit platform

Model and validate control solutions in simulation

• Build numerical models and simulations to test control strategies at scale
• Validate performance and robustness before deployment on hardware

Collaborate on hardware implementation

• Work with experimentalists and technical teams to implement control solutions
• Support testing and iteration of control strategies on the platform through to full delivery in production

Advance the company's control-theory knowledge and toolkit

• Provide a clear view of the state of the art for qubit control
• Strengthen the mathematical foundations underpinning control design

Requirements

• 4+ years of research experience in control theory or a related domain, post-PhD in applied mathematics, control theory, physics, or a closely related field
• Strong foundation in mathematical control theory (model-based and model-free optimal control, controllability, and reinforcement learning)
• Experience with control of dynamical systems
• Numerical modeling and simulation skills, with experience in scientific computing
• Familiarity with data-driven control methods and reachability analysis
• Ability to translate theoretical methods into applied, measurable engineering outcomes
• Strong collaboration skills across research, software, and hardware teams
• Rigorous and autonomous, with the ability to communicate complex results clearly
• Professional-level English proficiency, both written and spoken

Recruitment Process

• Screening Call with Grace, Talent Acquisition Specialist (30 min)
• Hiring Manager Interview with Gaspard (45 min)
• Technical Interview with the Team (60 min)
• Leadership Team Interview (30 min)
• Fit Interview (45 min)
• Reference check