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Ent is an intent-aware workspace security platform that protects human and AI-driven work by understanding not just what users and agents do, but why they do it. Founded by the co-founders of RiskIQ (acquired by Microsoft) and the team behind Microsoft Security Copilot, Ent is in production with Global 2000 customers across hospitality, financial services, and defense, backed by top-tier investors including Sequoia, Decibel, and In-Q-Tel.
We are seeking an ML Ops/Production Engineer passionate about scientific computing and data processing for AI systems. This role focuses on solving hard problems in the optimization and deployment of heterogeneous model sets (small, medium, and LLM-scale), ensuring robustness and long-term maintainability. You will innovate around frameworks that enable fast experimentation with large datasets, model training/validation, and CI/CD pipelines.
Your responsibilities will span development of production systems and supporting infrastructure for training, validation, and management of new models and datasets. You'll work on building and scaling distributed systems that handle complex ML workloads in a security-first context.
You bring experience building production-grade distributed systems for large-scale data processing, ML/AI, and LLMs. You're comfortable designing and building systems with ambiguous requirements, anticipating changes and expansion. You have a track record of delivering results in academic or industry settings.
Ent operates with a customer-first philosophy, building products around real problems security teams face. The culture emphasizes humility, teamwork, accountability, and urgency—moving at the speed of the people we protect. We're hiring the team that will define this category in the AI era.
