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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.
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.
Job Overview As a Principal Architect, you will lead the design and evolution of GreyOrange’s Multi-Agent Path Finding (MAPF) algorithm. You’ll define architectural direction, integrate machine learning techniques, and build the core capabilities required to achieve state-of-the-art and scalable navigation.
Key Responsibilities
Define the technical roadmap to push performance and scale in navigation
Lead design and implementation of ML model pipelines, including data ingestion, training, validation, deployment, and monitoring
Own production deployment of ML/RL models using MLOps tools such as Vertex AI or similar platforms
Integrate ML pipelines with robotic orchestration engines to support continuous learning and adaptation
Collaborate closely with software, robotics, product, and operations teams to align system goals with real-world fulfillment challenges
Required Qualifications
Education: M.S/Ph.D in Computer Science, AI/ML, or a related field
Experience: 10+ years of total experience in academia or industry
3>Technical Expertise
Strong knowledge of path planning, graph search algorithms, and optimization techniques for multi-agent systems
Deep understanding of machine learning, deep learning, and reinforcement learning
Experience using TensorFlow or PyTorch
Proven experience in building, deploying, and maintaining ML models in production environments
Hands‑on experience with MLOps, including CI/CD for models, pipeline orchestration, and model monitoring
Proficiency in Python. Familiarity with Erlang, Elixir, or other concurrency-first functional programming languages is good to have
Solid understanding of concurrency, parallelism, and real‑time systems
Strong CS fundamentals including algorithms, operating systems, networking, memory management, and performance tuning
Experience with distributed systems, microservices, and containerized deployments using Docker and Kubernetes
Knowledge of event‑driven architectures
Familiarity with cloud platforms such as Google Cloud Platform (GCP), and specifically Vertex AI
Nice to Have: Contributions to open‑source projects, research publications, or patents in MAPF, RL, or distributed AI systems
EEO GreyOrange provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.
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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.
Job Overview As a Principal Architect, you will lead the design and evolution of GreyOrange’s Multi-Agent Path Finding (MAPF) algorithm. You’ll define architectural direction, integrate machine learning techniques, and build the core capabilities required to achieve state-of-the-art and scalable navigation.
Key Responsibilities
Define the technical roadmap to push performance and scale in navigation
Lead design and implementation of ML model pipelines, including data ingestion, training, validation, deployment, and monitoring
Own production deployment of ML/RL models using MLOps tools such as Vertex AI or similar platforms
Integrate ML pipelines with robotic orchestration engines to support continuous learning and adaptation
Collaborate closely with software, robotics, product, and operations teams to align system goals with real-world fulfillment challenges
Required Qualifications
Education: M.S/Ph.D in Computer Science, AI/ML, or a related field
Experience: 10+ years of total experience in academia or industry
3>Technical Expertise
Strong knowledge of path planning, graph search algorithms, and optimization techniques for multi-agent systems
Deep understanding of machine learning, deep learning, and reinforcement learning
Experience using TensorFlow or PyTorch
Proven experience in building, deploying, and maintaining ML models in production environments
Hands‑on experience with MLOps, including CI/CD for models, pipeline orchestration, and model monitoring
Proficiency in Python. Familiarity with Erlang, Elixir, or other concurrency-first functional programming languages is good to have
Solid understanding of concurrency, parallelism, and real‑time systems
Strong CS fundamentals including algorithms, operating systems, networking, memory management, and performance tuning
Experience with distributed systems, microservices, and containerized deployments using Docker and Kubernetes
Knowledge of event‑driven architectures
Familiarity with cloud platforms such as Google Cloud Platform (GCP), and specifically Vertex AI
Nice to Have: Contributions to open‑source projects, research publications, or patents in MAPF, RL, or distributed AI systems
EEO GreyOrange provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.
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