Anduril Industries
Software Engineer, Battlespace Awareness
About this role
Anduril Industries is seeking a Software Engineer focused on Battlespace Awareness to develop innovative algorithms and software solutions for military applications. The role involves prototyping high-performance software, modeling and simulation analysis, and collaboration with customers to ensure successful mission-critical outcomes.
What you'll do
- Provide expertise and direction to a small team
- Prototype advanced software solutions in an agile environment
- Develop high-performance software for tactical and web systems
- Utilize modeling and simulation tools for technology analysis
- Engage with customers for effective mission support
- Manage the software development lifecycle
What they're looking for
- 2+ years of software engineering experience
- Proficiency in C/C++, Rust, Python, and Matlab
- Experience with big data and NoSQL technologies
- Software design and algorithm implementation
- Knowledge of machine learning techniques
- Strong applied mathematics skills
- Understanding of controls and digital signal processing
- Eligibility for U.S. Top Secret SCI clearance
Benefits
- Highly competitive equity grants
- Top-tier benefits for full-time employees
- Opportunities for professional development
- Work in an innovative and impactful environment
- Collaborative team culture
- Mission-driven company objectives
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Anduril Industries
Anduril Industries builds autonomous defense systems including underwater vehicles, unmanned aircraft, and electronic warfare platforms for the Department of Defense. The company is hiring across mechanical engineering, mission operations, software development, technical leadership, and advanced manufacturing roles to support the design, deployment, and production of these mission-critical systems.
- Website
- anduril.com
Likely interview questions
- Walk us through a complex software system you've designed and optimized for high performance. What were the key trade-offs you made?
- Describe your experience with machine learning in production systems. How have you handled model validation and performance monitoring?