STR's Intelligence Division researches, develops, and transitions advanced analytics and machine learning-based solutions to solve challenging problems related to national security.
Requirements
- Proven experience in developing bespoke machine learning algorithms, particularly in high-dimensional multivariate complex-valued functions.
- Strong proficiency in Python, with expertise in libraries such as PyTorch, TensorFlow.
- Usage experience with cloud computing platforms, preferably AWS
- Experience in database management, including SQL and NoSQL databases.
- Excellent understanding of mathematical concepts, including linear algebra, graph theory, and algebraic geometry.
- Knowledge of DevOps practices and CI/CD pipelines
Responsibilities
- Lead the development of machine learning and deep learning algorithms for large-scale multimodal data fusion problems
- Deep understanding of Generative AI (GenAI) models and reinforcement learning models to address specific business challenges
- Collaborate with other research scientists, software engineers, and mission partners to create robust solutions that leverage state-of-the-art deep learning and advanced data analysis techniques
- Work with large data sets in a parallel computing environment, utilizing tools such as AWS services (EC2, S3, etc.) for data storage and processing
- Stay up-to-date with the latest advancements in machine learning and artificial intelligence, and contribute to the development of novel algorithms and techniques
- Design and implement neural network architectures, including transformer-based architectures and autoencoders
- Refactor and optimize existing software for improved performance and scalability
Other
- Active Top Secret security clearance for which U.S. citizenship is needed by U.S. Government
- PhD, MS, or BS in Computer Science, EE, Physics, Mathematics, or a related field with 2-7+ years of relevant experience depending on your degree
- Lead a team of research scientists and software engineers, guiding project direction and ensuring successful project delivery
