Anduril Industries
Machine Learning Research Engineer
About this role
Anduril Industries seeks a Machine Learning Research Engineer to develop and deploy edge-compatible AI systems for military applications. You'll work on fine-tuning transformer models, building LLM-based autonomous systems, and collaborating across teams to solve real-world defense challenges.
What you'll do
- Optimize and deploy transformer architectures for edge devices and disconnected environments
- Design and prototype LLM-based agentic systems for military applications
- Create and maintain performance benchmarks for mission-critical ML models
- Partner with cross-functional teams to identify and scope new research problems
- Develop and manage ML algorithms in production environments
What they're looking for
- Machine learning algorithm development and optimization
- Python and PyTorch proficiency
- Transformer model fine-tuning
- Edge device and air-gapped deployment experience
- ML benchmarking and evaluation
- Production ML systems management
- Generative AI and LLM knowledge
- Deep learning fundamentals
Benefits
- Competitive salary range: $220,000–$292,000 USD
- Equity grants included in most full-time offers
- Comprehensive health and wellness benefits
- Top-tier benefits package at minimal cost to employees
- Defense technology and startup work environment
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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 your experience fine-tuning transformer models. What challenges did you face optimizing them for edge devices or compute-constrained environments?
- Describe a machine learning system you've deployed to production. How did you handle monitoring, benchmarking, and iterating on it?