Principal Scientist

GreyOrange • Redwood City, CA

4d

About The Position

About GreyOrange GreyOrange is a global leader in AI-driven robotic automation software and hardware, transforming distribution and fulfillment centers worldwide. Our solutions increase productivity, empower growth and scale, mitigate labor challenges, reduce risk and time to market, and create better experiences for customers and employees. Founded in 2012, GreyOrange is headquartered in Atlanta, Georgia, with offices and partners across the Americas, Europe and Asia. For more information, visit www.greyorange.com . Our Solutions The GreyMatter Multiagent Orchestration (MAO) platform provides vendor agnostic fulfillment orchestration to continuously optimize performance in real time: the right order, with the right bot and agent, taking the right path and action. Currently operating more than 70 fulfillment sites across the globe (with deployments of 700+ robots at a single site), GreyMatter enables customers to decrease their fulfillment Cost Per Unit by 50%, reduce worker onboarding time by 90% and optimize peak season performance. In retail stores, our gStore end to end store execution and retail management solution supports omnichannel fulfillment, real time replenishment, intelligent workforce tasking and more. Using real time overhead RFID technology, the platform increases inventory accuracy up to 99%, doubles staff productivity, and enables an engaging, seamless in store experience. About the job We are seeking a highly skilled and innovative Principal Scientist – Operations Research & Simulation to lead the development of advanced simulation platforms and optimization algorithms for complex, automated fulfillment environments. This role requires a deep understanding of operations research, optimization theory, and simulation modeling, with a strong focus on solving NP-hard scheduling and orchestration challenges.

Requirements

• Deep understanding of operations research, optimization theory, and simulation modeling.
• Strong focus on solving NP-hard scheduling and orchestration challenges.
• Expertise in simulation frameworks and tools (e.g., AnyLogic, FlexSim, Simio, or custom-built environments in Python/C++).
• Strong programming skills in Python, R, and SQL.
• Deep domain knowledge of automated warehouse operations, robotics orchestration, and human-machine interaction.

Responsibilities

• Lead the design and development of advanced simulation and emulation platforms that accurately replicate complex fulfillment environments, including automation systems, human workflows, and software orchestration layers.
• Architect and implement robust optimization algorithms utilizing operations research techniques such as linear programming, dynamic programming, heuristics, and stochastic models.
• Develop and apply solutions for NP-hard problems related to scheduling, resource allocation, and orchestration using constraint-based and temporal planners, as well as commercial solvers (e.g., Gurobi, CPLEX).
• Conduct in-depth analysis of large-scale performance data to identify inefficiencies, extract actionable insights, and recommend data-driven improvements in throughput, accuracy, and system stability.
• Collaborate cross-functionally with software engineering, robotics, and operations teams to integrate simulation models and optimization algorithms into live production systems.
• Design and validate digital twins that offer predictive capabilities and support scenario planning and what-if analysis for future deployments.
• Leverage expertise in simulation frameworks and tools (e.g., AnyLogic, FlexSim, Simio, or custom-built environments in Python/C++) to develop high-fidelity models for continuous performance evaluation.
• Develop and maintain analytics solutions, data pipelines, and optimization engines using strong programming skills in Python, R, and SQL.
• Apply deep domain knowledge of automated warehouse operations, robotics orchestration, and human-machine interaction to ensure simulation fidelity and real-world applicability.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: No Education Listed