Captivation Software
Artificial Intelligence Machine Learning Engineer 2
About this role
Mid-level AI/ML engineer role focused on designing, developing, and operationalizing machine learning algorithms for mission-critical applications. You'll handle the full lifecycle from data selection and statistical analysis through model testing, optimization, and production deployment.
What you'll do
- Design and develop AI/ML algorithms to address business and mission challenges
- Conduct statistical analysis, data selection, and feature engineering for model training
- Implement machine learning algorithms using various libraries and evaluate performance through testing protocols
- Optimize model performance through iterative refinement and retraining based on evaluation results
- Transform data science prototypes into scalable, production-ready solutions with proper architecture
- Oversee system integration, verification, and validation to meet requirements
What they're looking for
- Machine learning algorithms and frameworks (TensorFlow, PyTorch, scikit-learn, etc.)
- Statistical analysis and data preprocessing
- Python or similar ML-focused programming languages
- Data modeling and software architecture
- Model evaluation, testing, and performance optimization
- Data quality assurance and validation
- System integration and deployment
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Captivation Software
Captivation Software builds high-performance computing and secure analytics infrastructure for mission-critical government programs, specializing in HPC testing, enterprise dataflow architectures, and cleared defense systems. The company is hiring senior test engineers, systems engineers, and technical leaders with deep expertise in Linux, cloud infrastructure, and security clearances to validate systems and architect solutions for intelligence community operations.
View all jobs at Captivation SoftwareLikely interview questions
- Walk us through your experience taking a machine learning prototype from research to production—what were the key challenges?
- How do you approach detecting and addressing data distribution shifts that impact model performance?