Senior Manager, AI Platform Engineering

True Anomaly•Long Beach, CA

6h•$200,000 - $290,000•Onsite

About The Position

A new space race has begun. True Anomaly seeks those with the talent and ambition to build innovative technology that solves the next generation of engineering, manufacturing, and operational challenges for space security and sustainability. OUR MISSION The peaceful use of space is essential for continued prosperity on Earth—from communications and finance to navigation and logistics. True Anomaly builds innovative technology at the intersection of spacecraft, software, and AI to enhance the capabilities of the U.S., its allies, and commercial partners. We safeguard global security by ensuring space access and sustainability for all. OUR VALUES Be the offset. We create asymmetric advantages with creativity and ingenuity What would it take? We challenge assumptions to deliver ambitious results It’s the people. Our team is our competitive advantage and we are better together YOUR MISSION 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.

Requirements

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
U.S. citizenship required; clearance eligibility required; active clearance at any level preferred

Nice To Haves

Experience deploying AI systems in defense, government, or regulated environments with data handling requirements (CUI, ITAR, FedRAMP, or DoD Impact Levels)
Background with enterprise AI platforms (Anthropic Claude, OpenAI, Google Gemini) including API management, rate limiting, cost controls, and multi-provider strategies
Experience with on-premises GPU infrastructure for model hosting and inference where cloud-only is not an option
Familiarity with knowledge management platforms (Glean or similar) and experience connecting them to enterprise data sources
Prior experience driving AI adoption at a company where AI fluency was treated as an organizational competency, not just an engineering initiative
Active U.S. security clearance at any level

Responsibilities

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