The role involves leading the Power Applications team in the end-to-end lifecycle of power control and orchestration applications, from algorithm development to production maintenance. You will coordinate cross-functional teams, define engineering standards, and act as the primary technical interface for system architecture.
The Principal Architect will own the end-to-end architecture of the Karman Digital Environment (KDE) simulation platform to enable scalable development and validation. They will lead an engineering team while serving as the technical authority for simulation methodology across hardware, firmware, and algorithm teams.
The Director of Talent Management will build and lead the in-house talent acquisition function, managing full lifecycle recruiting for technical and non-technical roles. Additionally, the role involves strategic headcount planning, organizational development, and implementing HR technology to support company growth.
Design, build, and operate an off-device platform that ingests and processes data from edge AI devices across cloud and on-premises environments. Lead the technical direction for infrastructure, automate operational tasks, and mentor engineers to ensure platform resilience and scalability.
The role focuses on designing and validating electrical power integration, redundancy, and sequencing for Karman hardware within customer data centers. It involves creating installation procedures, executing deployments, and serving as a technical bridge between customers and core engineering.
Build the integration layer between the Karman platform and diverse customer data center software environments through custom API connectors and test frameworks. Collaborate with customer operations teams to translate technical requirements into actionable integration designs and scalable playbooks.
Lead the intelligence layer of the Karman platform, overseeing algorithms, control methods, and optimization techniques for AI data center power systems. Partner with Engineering and Product leaders to translate research into production-ready capabilities that deliver quantifiable customer value.
The AI Infrastructure Engineer will design and own the end-to-end infrastructure for serving AI and ML models across edge, cloud, and data center environments. They will also collaborate with engineering teams to integrate power data with AI inference software and optimize GPU performance.
