Mid-Level Software Engineer

Riverside Research Institute•Fairfax, VA

2d•$90,000 - $180,000

About The Position

As a Mid-Level Machine Learning Software Engineer, you will be responsible for developing and implementing machine learning algorithms and solutions to address complex challenges within defense applications. You will collaborate closely with cross-functional teams, including data scientists, software engineers, and defense analysts, to enhance the capabilities of our systems and support mission-critical operations.

Requirements

Current TS/SCI clearance
Bachelor's or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
8-10 years of experience in software engineering with a focus on machine learning and data analysis.
Proficient in programming languages such as Python, Java, or C++, with experience in machine learning libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
Strong understanding of machine learning algorithms, statistical analysis, and data mining techniques.
Experience with data management tools and frameworks, such as SQL, Hadoop, or Spark.
Familiarity with software development methodologies (Agile, Scrum) and version control systems (Git).
Excellent problem-solving skills and the ability to work effectively in a team-oriented environment.
Effective communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.

Nice To Haves

Experience in defense or government contracting environments.
Knowledge of cybersecurity principles and practices.
Familiarity with cloud computing platforms (e.g., AWS, Azure) and deployment of machine learning models in cloud environments.

Responsibilities

Design, implement, and optimize machine learning models for various defense applications, including data analysis, pattern recognition, and predictive modeling.
Collaborate with data scientists to preprocess and analyze large datasets, ensuring high-quality training data for model development.
Develop scalable software solutions that integrate machine learning algorithms into existing systems and workflows.
Conduct experiments to evaluate and refine model performance, ensuring reliability and accuracy.
Participate in the full software development lifecycle, including requirements gathering, design, implementation, testing, and documentation.
Stay current with advancements in machine learning technologies and methodologies, applying best practices to enhance our projects.
Contribute to the preparation of technical reports and presentations for stakeholders.