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Lodestar

Software Engineer: On-board Autonomy

Los Angeles, US$120k–$145kmidAdded 1 month ago

About this role

Lodestar is seeking a Software Engineer specializing in on-board autonomy to develop decision-making systems for their AI-powered autonomy suite. The role involves creating algorithms for real-time autonomous decision-making, with a focus on adaptability in complex mission scenarios.

What you'll do

  • Design and implement on-board decision-making models
  • Develop autonomous decision algorithms integrating multiple information sources
  • Research and implement machine learning models for decision making
  • Create adaptive decision models for changing mission contexts
  • Build frameworks for continuous strategy re-evaluation
  • Maintain autonomy infrastructure and deployment pipelines

What they're looking for

  • Bachelor’s or Master’s degree in a relevant field
  • 2+ years of experience in aerospace/robotics autonomy
  • Proficiency in C++ and Python
  • Experience with machine learning in decision-making
  • Background in optimal control and planning
  • Familiarity with multi-agent decision-making
  • Experience with real-time systems and performance optimization
  • Understanding of distributed autonomy and networking

Benefits

  • Competitive salary based on experience level
  • Equity incentives in the company
  • Flexible paid time off and holidays
  • Collaborative engineering culture
  • [unknown]
  • [unknown]
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Lodestar

Lodestar develops MITHRIL, an AI-powered autonomy suite for autonomous spacecraft operations that combines state estimation, perception, and real-time decision-making capabilities. The company is hiring Software Engineers specializing in state estimation, perception algorithms, and on-board autonomy to build advanced systems for space target detection, trajectory prediction, and autonomous mission execution.

View all jobs at Lodestar

Likely interview questions

  • Walk us through a decision-making or control system you've built for autonomous systems. How did you handle real-time constraints and uncertainty?
  • Describe your experience implementing machine learning models for decision-making or planning. What was your approach from literature review through deployment?
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