Deep Reinforcement Learning Specialist

Deep Reinforcement Learning Specialist

Requirements

• Bachelor’s degree in STEM, emphasis Computer Science, Electrical or Computer Engineering, or related field(s)
• Typically, 8 or more years of experience in relevant fields in industry
• Software development skills in at least two different programming languages (e.g., Python, R, C/C++)
• Experience with reinforcement learning libraries in Python
• Experience using deep learning packages (TensorFlow, PyTorch, JAX)
• Experience cleaning data, prototyping, and fine-tuning state of the art deep learning models
• Experience with statistics, machine learning, and reinforcement learning
• Experience with software engineering and software development best practices
• Familiarity with Unix/Linux operating systems
• Demonstrated technical leadership in one or more reinforcement learning projects
• Demonstrated ability to efficiently deliver analysis and insights from data that is large, unstructured, or unprocessed
• Demonstrated ability to operate independently and proactively seek guidance as needed
• Demonstrated ability to communicate technical material to a non-technical audience
• This position requires ability to obtain and maintain a security clearance, which is issued by the US government. U.S citizenship is required to obtain a security clearance.

Responsibilities

• Evaluate technologies for use in scalable and resilient mission-critical applications in a production environment
• Champion the deep reinforcement learning and data science bridge between data subject matter experts and customer stakeholders
• Tell stories that describe insights and analytical findings both to engineers and to customer stakeholders
• Build and lead small, innovative teams to develop algorithms and application prototypes
• Fuse together various data sources
• Create dynamic data visualizations
• Foster an environment of continual learning

Preferred Qualifications

• An advanced degree in Mechanical Engineering, Mathematics, Physics, Electrical Engineering, Computer Science, or related field(s)
• Previous publications in journals/conferences related to machine learning, engineering, or the physical sciences
• Experience with multi-agent reinforcement learning
• Experience with dynamical systems theory, familiarity with concepts and trade-offs associated with stability
• Experience with optimal, robust, and/or nonlinear control
• Demonstrated contributions to open-source software repositories
• Familiarity with containerization technologies (e.g., Docker, Kubernetes, Rancher, etc.)
• Familiarity deploying machine learning models on cloud platforms (e.g., AWS, Azure)
• Experience implementing real-time reinforcement learning algorithms in large scale environments
• Participation in extracurricular activities related to data science (e.g., Kaggle competitions)
• Active security clearance