Machine Learning Engineer IV

About the position

General Atomics Aeronautical Systems, Inc. (GA-ASI), an affiliate of General Atomics, is a world leader in proven, reliable remotely piloted aircraft and tactical reconnaissance radars, as well as advanced high-resolution surveillance systems. We have an exciting opportunity for a Machine Learning Engineer in Poway, CA. The Autonomy and Artificial Intelligence Solutions Software group is charted to develop and deploy end-to-end autonomous systems that enable unmanned aerial systems (UAS) to execute autonomous missions.

Responsibilities

• Develops and communicates descriptive, diagnostic, predictive and prescriptive insights/algorithms of limited scope.
• Uses machine language and statistical modeling techniques to develop and evaluate algorithms to improve product/system performance, quality, data management and accuracy.
• Translates algorithms and technical specifications into code using programming languages and technologies.
• Completes programming, implements efficiencies, and performs testing and debugging.
• Completes documentation and procedures for installation and maintenance.
• Applies deep learning technologies to enable computers to visualize, learn and respond to situations of limited scope.
• Leads technical teams and scopes challenging projects into executable sprints.
• Drives code reviews to help the team adhere to general DevSecOps and MLOps best practices.
• Adapts machine learning to areas such as virtual reality, augmented reality, artificial intelligence, robotics and other products for interactive experiences.
• Interfaces with external vendors and partners to integrate their technology into the team's autonomy stack.
• Maintains the strict confidentiality of sensitive information.
• Performs other duties as assigned.
• Responsible for observing all laws, regulations and other applicable obligations wherever and whenever business is conducted on behalf of the Company.

Requirements

• Experience with machine learning and statistical modeling techniques such as decision trees, logistic regression, and Bayesian analysis.
• Proficiency in programming languages relevant to machine learning and software development.
• Experience in testing, debugging, and documenting software solutions.
• Ability to lead technical teams and manage project scopes effectively.
• Strong understanding of DevSecOps and MLOps best practices.

Nice-to-haves

• Experience in applying machine learning to virtual reality and augmented reality applications.
• Familiarity with robotics and interactive user experiences.