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3 days ago Be among the first 25 applicants We are looking to hire a Principal Architect-ML for our client, a robotics unicorn based in Silicon Valley. Backed by over $500 million in funding from top-tier investors, our client is at the forefront of developing cutting-edge machine learning technology. They are building pioneering intelligent automation systems that power some of the world’s largest warehouses and retail operations. By seamlessly combining real-time decision-making algorithms with advanced robotics, our client enables global enterprises to operate faster, smarter, and more efficiently—reshaping the future of supply chain and fulfillment. As a Principal Architect, you will lead 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. This is a hands-on, technically-deep role for candidates with strong expertise in reinforcement learning (RL) and deep learning (DL). Experience with generic AI or MLOps without RL/DL depth will not be considered a fit. This role will be responsible for building the AI models from scratch and driving key initiatives in partnership with Google. Key Responsibilities Architect distributed systems to support real-time path planning of multi-agent using Machine Learning. Lead design and implementation of ML model pipelines, including data ingestion, training, validation, deployment, and monitoring. Provide technical leadership in building RL/DL-based models that drive autonomous decision-making in a distributed, multi-agent system. Lead initiatives from scratch, ensuring technical rigor in model design, experimentation, and deployment. 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: B.E/M.S in Computer Science, AI/ML, or a related field. Experience: 12+ years of total experience (7+ years of experience in system architecture & AI/ML ; including 2+ years in technical leadership roles). 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, with experience using TensorFlow or PyTorch.

Hands-on RL/DL development experience is essential – candidates must have built and deployed such models at scale. Proven experience in building, deploying, and maintaining ML models in production environments. Prior experience in building from scratch rather than just maintaining existing systems will be valued. 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. Seniority level

Seniority level Mid-Senior level Employment type

Employment type Full-time Job function

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