Senior Manager of AI Platform Engineering, True Anomaly

• 8+ years of experience in software engineering, ML engineering, or AI infrastructure, with 3+ years building and leading engineering teams
• Hands-on experience building AI/ML infrastructure: model serving, GPU compute management, inference pipelines, and the platform layer that production AI systems run on
• Experience building agent systems or AI-powered automation that interacts with external systems via APIs, manages state, and operates with appropriate human oversight
• Track record of hiring and developing high-performing engineering teams in fast-paced environments
• Strong understanding of current AI tooling: frontier models, enterprise AI platforms, coding assistants, knowledge systems, and how they fit together in a production environment
• Ability to work across technical and non-technical stakeholders to identify AI use cases, set priorities, and deliver capabilities that people actually adopt

Desirable

• True Anomaly is becoming an AI-first company. Every function, from engineering to manufacturing to business operations, is expected to work with AI as a core part of their workflow. There is no AI engineering team today, no internal model hosting infrastructure, and no agent platform
• As Senior Manager of AI Engineering, you will build all of it. You will own the AI platform: model hosting, agent frameworks, enterprise AI tooling, and the infrastructure that connects AI capabilities to the systems where work happens. You will also own AI enablement across the company, embedding with teams to identify high-value use cases and deploying the tools and patterns that make adoption stick
• This is a player-coach role with full executive sponsorship. You will be making architecture decisions and shipping code on day one while building a team of 8 engineers around you
• Build and lead the AI Engineering team from zero: 8 engineers spanning AI platform, AI infrastructure, and enterprise AI tooling, with an emphasis on recruiting people who can ship in ambiguous environments with minimal direction
• Own the AI platform architecture: model hosting (cloud and on-premises GPU compute), agent frameworks, API gateways, and the platform layer that connects AI capabilities to the tools and systems where work happens
• Build and deploy enterprise AI tooling across the company: knowledge platforms (Glean or equivalent), coding assistants, MCP connectors, and AI-powered workflows embedded in collaboration and engineering tools
• Design and build the agent infrastructure that moves the company from standalone AI tools to embedded and autonomous AI: agents that monitor, analyze, recommend, and act within company systems with appropriate human oversight
• Drive AI enablement across the organization: training, embedding with teams, supporting a distributed champion model, and building the reusable patterns and documentation that make adoption self-sustaining
• Partner with Data Engineering (critical-path dependency for the AI platform), Infrastructure Engineering (compute and networking), and Cybersecurity (compliance and data handling) to deliver a platform that is secure, scalable, and production-ready