Principal GNC Scientist – GPS-Denied Navigation & Control

Grvty

California

On-site

USD 270,000 - 300,000

Full time

6 days ago
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Benefits offered by this job

Competitive pay
Comprehensive benefits
Professional growth opportunities

Job summary

GRVTY is seeking a Principal Guidance, Navigation & Controls (GNC) Scientist to serve as a technical authority for autonomous vehicle GNC capabilities and advanced navigation products and software. In this role you will lead the design and analysis of navigation and control algorithms, drive technical strategy across multiple concurrent initiatives, and mentor engineers.

You will address the boundary of theory and field-proven implementation, translating advanced control theory and estimation

Qualifications

  • PhD in Aerospace Engineering or closely related field is required.
  • Deep expertise in state estimation (Kalman filtering, sensor fusion) and modern control theory.
  • Strong MATLAB/Simulink proficiency and software development skills.
  • Excellent written and verbal communication for technical and customer audiences.

Responsibilities

  • Lead design, analysis, and validation of GNC algorithms.
  • Architect navigation solutions for GPS-denied environments.
  • Develop and refine vehicle dynamics models and control architectures.
  • Serve as the senior technical authority on GNC across multiple platforms.
  • Mentor mid-level and junior GNC engineers.

Skills

State estimation
Modern control theory
Signal processing
Communication skills

Education

PhD in Aerospace Engineering or closely related field

Tools

MATLAB/Simulink
C/C++
Real-time embedded software environments

Job description

GRVTY is seeking a Principal Guidance, Navigation & Controls (GNC) Scientist to serve as a technical authority for autonomous vehicle GNC capabilities and advanced navigation products and software. In this role you will lead the design and analysis of navigation and control algorithms, drive technical strategy across multiple concurrent initiatives, and mentor engineers.

You will address the boundary of theory and field-proven implementation, translating advanced control theory and estimation

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