Applied Reinforcement Learning Scientist

Are you passionate about driving innovation through machine learning? Do you thrive in building and optimizing RL systems? If so, we have an exciting opportunity for you as a founding team member and Reinforcement Learning Scientist at fast growing incubation.  

We’re currently looking for creative, smart people who want to join our team of world-renowned researchers as we push the boundaries applying AI to complex problems. As a Senior Analyst specializing in Reinforcement Learning, you will be at the forefront of driving the development and optimization of our RL models. You will play a crucial role in building and training Reinforcement Learning Models and designing feedback systems to continuously improve their performance. If you're a forward-thinking, technically adept individual with a passion for machine learning, this role is for you.  

Responsibilities:  

• Integrate with an interdisciplinary team of experts in optimization and simulation to design, build, and optimize models to deliver high-quality, real-world results.  
• Design and implement novel RL algorithms and approaches to solve complex problems, ensuring efficiency and scalability.  
• Develop and implement feedback mechanisms to continuously improve the accuracy and effectiveness of RL models.  
• Stay up-to-date with the latest advancements in reinforcement learning and implement best practices to enhance our ML infrastructure.  
• Communicate complex technical concepts and findings to non-technical stakeholders in a clear and concise manner.  
• Collaborate with cross-functional teams to understand business requirements and translate them into actionable ML solutions.  

Requirements:  

• Proven experience in a leadership role driving RL system development and optimization.  

• Demonstrated expertise in building RL models for complex real-world problems and building robust training pipelines.  
• Extensive experience implementing reinforcement learning in complex domains – preferably with multi-agent systems or models that require temporal abstraction. 
• Proficiency in programming languages like Python, Java or C++ and the ability to write efficient, clean, and maintainable code.  
• Excellent written and verbal communication skills, with the ability to convey technical concepts to both technical and non-technical audiences.  
• A Master's degree in Computer Science, Engineering, or a related field. A Ph.D. with a focus in Reinforcement Learning or a relevant area with a publication history is ideal.  

• U.S. citizenship is required   

Benefits:  

• Competitive salary and performance-based bonuses.  

• Comprehensive health and wellness benefits package.  
• Flexible work hours.  
• Opportunities for professional development and continued learning.  
• Collaborative and inclusive work environment.