Machine Learning Specialist/Architect

Principal Architect – MAPF As a Principal Architect, you will lead the design and evolution of intelligent, scalable systems for Multi-Agent Path Finding (MAPF). You’ll define architectural direction, integrate machine learning and reinforcement learning, and build the core capabilities required to achieve safe, efficient, and autonomous decision-making at scale. Responsibilities

Lead the design and evolution of scalable MAPF systems. Define architectural direction and integrate machine learning and reinforcement learning. Build core capabilities to enable safe, efficient, autonomous decision-making at scale. Qualifications

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, with 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. 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.

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