Principal Software Engineering Manager - Substrate efficiency

Build and lead a high-performing engineering team focused on inference runtime efficiency and model execution performance. Define and drive strategy to improve throughput per GPU through runtime optimizations. Increase engineering agility, enabling faster experimentation, iteration, and rollout of performance improvements. Establish metrics, telemetry, and experimentation frameworks to measure efficiency gains and guide investment decisions. Own live-site performance, reliability, and operational excellence for inference engines at scale. Drive alignment across partner teams on engine interfaces, performance goals, and optimization priorities. Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python These requirements include but are not limited to the following specialized security screenings: Master's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 15+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. 6+ years people management experience. Experience leading engineering teams building backend or distributed systems. Hands-on experience improving system throughput, performance, and resource utilization across large-scale infrastructure. Systems thinking, with the ability to identify and optimize bottlenecks across execution, scaling, and resource management. Experience driving system-level improvements in areas such as workload execution, scheduling, batching, or infrastructure efficiency. Able to translate technical insights into clear engineering priorities and execution plans. Comfortable collaborating across teams to align on goals and execution. Experience with developing AI/ML inference systems or GPU-based workloads. Familiarity with inference or training runtime optimization techniques. Experience improving throughput per resource (e.g., cost per query) in large-scale systems.