ProDex Labs
Company Description
ProDex Labs is pioneering the fastest and most intuitive way to simulate and optimize operational decisions for factories and depots. At the core of our solutions is an AI-native discrete event simulation engine capable of modeling complex systems in real time, including processes, constraints, resources, and data. By leveraging AI-powered conversational interfaces, we enable operators to create simulations, optimize production schedules within seconds, and test scenarios for better decision-making. Our mission is to revolutionize decision-making for complex operations, moving from reactive approaches to proactive, data-focused optimization. With a vision to lead the next industrial revolution, we aim to establish a standard for integrating digital twins and human-in-the-loop AI in the defense and manufacturing sectors.
Design and implement the mathematical and simulation engines that power our factory planning tools, and expose them through performant APIs and intuitive interfaces. Blend operations research, backend development, and modern web development.
This is ProDex’s computational backbone and the connection between operations research and user experience: where modeling, optimization, and product meet. You’ll design and implement the mathematical and simulation engines that power our factory planning tools, and expose them through performant APIs and intuitive interfaces. The work blends operations research, backend development, and modern web development — turning optimization models into real, usable products. You’ll be equally comfortable writing an optimization solver in Python as you are wiring its results into a FastAPI service or a React frontend.
Responsibilities:
Develop and maintain mathematical models for complex manufacturing constraints and production rules
Design and implement optimization algorithms for factory scheduling and resource allocation
Build RESTful or GraphQL APIs with FastAPI to expose modeling capabilities
Translate messy, real-world production requirements into robust, solvable mathematical problems
Implement front-end visualizations using React + TypeScript to display optimization results
Build and refine discrete-event simulation engines for production planning scenarios
Optimize solver performance for both speed and accuracy in production environments
Collaborate with customers to understand domain-specific constraints and encode them into the system
Basic Qualifications:
Strong foundation in applied math, statistics, or operations research
Proficiency in Python for modeling, simulation, and backend development
Experience building RESTful or GraphQL APIs with FastAPI (or Flask)
Strong understanding of concurrency, synchronization, and parallel execution
Working knowledge of React + TypeScript for front-end development and visualization
Ability to translate messy, real-world constraints into solvable, production-ready logic
Comfort collaborating across modeling, design, and infrastructure teams
Preferred Skills and Experience:
Familiarity with cloud-native deployment (Docker, Kubernetes, GCP/AWS)
Experience integrating optimization engines (OR-Tools, Pyomo, CPLEX, Gurobi, etc.)
Exposure to stochastic or Monte Carlo methods for uncertainty modeling
Experience building interactive data visualizations (Plotly, D3, or similar)
Background in logistics, defense, or discrete manufacturing
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Design and implement the mathematical and simulation engines that power our factory planning tools, and expose them through performant APIs and intuitive interfaces. Blend operations research, backend development, and modern web development.
This is ProDex’s computational backbone and the connection between operations research and user experience: where modeling, optimization, and product meet. You’ll design and implement the mathematical and simulation engines that power our factory planning tools, and expose them through performant APIs and intuitive interfaces. The work blends operations research, backend development, and modern web development — turning optimization models into real, usable products. You’ll be equally comfortable writing an optimization solver in Python as you are wiring its results into a FastAPI service or a React frontend.
Responsibilities:
Develop and maintain mathematical models for complex manufacturing constraints and production rules
Design and implement optimization algorithms for factory scheduling and resource allocation
Build RESTful or GraphQL APIs with FastAPI to expose modeling capabilities
Translate messy, real-world production requirements into robust, solvable mathematical problems
Implement front-end visualizations using React + TypeScript to display optimization results
Build and refine discrete-event simulation engines for production planning scenarios
Optimize solver performance for both speed and accuracy in production environments
Collaborate with customers to understand domain-specific constraints and encode them into the system
Basic Qualifications:
Strong foundation in applied math, statistics, or operations research
Proficiency in Python for modeling, simulation, and backend development
Experience building RESTful or GraphQL APIs with FastAPI (or Flask)
Strong understanding of concurrency, synchronization, and parallel execution
Working knowledge of React + TypeScript for front-end development and visualization
Ability to translate messy, real-world constraints into solvable, production-ready logic
Comfort collaborating across modeling, design, and infrastructure teams
Preferred Skills and Experience:
Familiarity with cloud-native deployment (Docker, Kubernetes, GCP/AWS)
Experience integrating optimization engines (OR-Tools, Pyomo, CPLEX, Gurobi, etc.)
Exposure to stochastic or Monte Carlo methods for uncertainty modeling
Experience building interactive data visualizations (Plotly, D3, or similar)
Background in logistics, defense, or discrete manufacturing
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