Applied Scientist

About DEFCON AI 

DEFCON AI, an insights company, is developing a next-generation Modeling, Simulation, and Analysis toolset tailored for mobility and logistics operations across various environments. Leveraging our extensive expertise in Artificial Intelligence, mathematical optimization, and software engineering, we are poised to revolutionize the industry. Our technology provides key insights that empower our customers to formulate strategies for mobility, sustainment, and logistics operations - equipping them with tools to visualize, assess, and mitigate the impacts of disruptions in their networks. 

About the Applied Scientist  

Are you passionate about driving innovation through advanced mathematical modeling? Does working in a multi-disciplinary team that addresses a diverse range of complex problems get you excited? If so, we have an exciting opportunity for you as an Applied Scientist in the Tech Development and Analytics team at DEFCON AI.   

We’re currently looking for creative, smart, and humble teammates who want to join our group of world-renowned researchers as we push the boundaries applying AI to complex problems. As an Applied Scientist, you will be at the forefront of driving the development and deployment of our Reinforcement Learning models. If you're a forward-thinking, technically adept individual with a passion for mathematical modeling and optimization, this role is for you. 

Responsibilities: 

• Integrate with an interdisciplinary team of experts in optimization and simulation to design, build, and optimize models that deliver high-quality, real-world insights.   
• Design and implement novel Reinforcement Learning algorithms and approaches to solve sequential decision problems, ensuring efficiency and scalability. 
• Stay up to date with the latest advancements in reinforcement learning and implement best practices to enhance our ML infrastructure.   
• The communication of complex technical concepts and findings to non-technical stakeholders clearly and concisely.   
• Collaborate with cross-functional teams to understand business requirements and translate them into actionable solutions.   

Qualifications:  

• Demonstrated expertise in building mathematical models for complex real-world problems 
• The ability to write efficient, clean, and maintainable code in one or many programming languages with the ability and willingness to learn more (Python, Java, Rust, C++, etc.) 
• Excellent written and verbal communication skills - with emphasis on the ability to effectively communicate highly technical concepts to both technical and non-technical audiences alike 
• An advanced degree in Computer Science, Engineering, or a related technical field. Alternative to traditional educational pathways, a well-documented portfolio of projects you could share and speak to is acceptable. 
• U.S. citizenship is required    

Preferred Qualifications: 

• Leadership experience driving Reinforcement Learning system development  
• Experience implementing Reinforcement Learning in complex domains – preferably with collaborative multi-agent systems 
• Experience with cloud-based SAAS products and pipelines 
• A Ph.D. with a focus on Reinforcement Learning or a relevant area (Markov Decision Processes, Sequential Decision Analytics, Approximate Dynamic Programming, Robust Optimization, etc.)  
• Publications in scientific journals and/or conferences are preferred 

Benefits:  

• 100% employer paid, comprehensive health care including medical, dental, and vision for you and your family. 
• Paid maternity and paternity for 14 weeks at employees' normal pay. 
• Unlimited PTO, with management approval. 
• A fully remote, results-based work environment. 
• Opportunities for professional development and continued learning. 
• Occasional travel to our offices in Washington D.C or alternative locations to collaborate with your team in person. 
• Optional 401K, FSA, and equity incentives available. 

Salary Range: $150,000-$400,000. This represents the typical slary range for this position based on experience, skills, and other factors.

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