Principal Architect

Job Description Our client is a global leader in AI-driven robotic automation software and hardware, transforming distribution and fulfillment centers worldwide. Their solutions increase productivity, empower growth and scale, mitigate labor challenges, reduce risk and time to market, and create better experiences for customers and employees. Our Solutions Their 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, their 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 their 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. Salary Range: 260,000-280,000K We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to View email address on click.appcast.io learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: Skills and Requirements 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. 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. Insight Global