Anduril Industries
Software Engineer, Battlespace Awareness
About this role
Anduril Industries is seeking a Software Engineer for their Battlespace Awareness team to develop innovative software solutions that enhance military capabilities. The role involves collaboration, prototyping, and engagement with customers to deliver high-performance software in a fast-paced environment.
What you'll do
- Lead direction and contribute expertise within a small team
- Prototype advanced software solutions in an agile setting
- Implement high-performance software from tactical systems to web applications
- Utilize modeling and simulation tools for technology benefit analysis
- Engage with customers for mission-critical outcomes
- Participate in all stages of the software development lifecycle
What they're looking for
- 5+ years of software engineering experience
- Proficient in C/C++, Python, and Matlab
- Experience with big data and database technologies
- Strong skills in software design and algorithm implementation
- Knowledge of machine learning techniques
- Understanding of applied mathematics and engineering principles
- Ability to obtain U.S. Top Secret SCI security clearance
Benefits
- Competitive salary range between $165,000 to $218,000
- Equity grants for full-time employees
- Comprehensive benefits package
- Opportunity to work on mission-critical technology
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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 implemented. How did you approach the architecture, and what trade-offs did you make?
- Describe your experience with machine learning pipelines. Can you give a specific example of a supervised or unsupervised learning problem you solved and how you validated your results?