Senior Manager, AI Platform Engineering

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

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

Experience deploying AI systems in defense, government, or regulated environments with data handling requirements (CUI, ITAR, FedRAMP, or DoD Impact Levels)

Base Salary:

Long Beach - $210,000 to $290,000, Denver - $200,000 to $280,000

Equity + Benefits

including Health, Dental, Vision, HRA/HSA options, PTO and paid holidays, 401K, Parental Leave

Your actual level and base salary will be determined on a case-by-case basis and may vary based on the following considerations: job-related knowledge and skills, education and experience.

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