Principal Software Engineer (Platform Architecture & Execution Model)

About Trase

Co-founded in 2023 by Joe Laws and Grant Verstandig, Trase Systems is AI, Uncomplicated. Trase empowers enterprise leaders to harness the full potential of AI without the associated complexity and risks. We are an end-to-end solution for deploying, managing, and optimizing AI in the enterprise. Our platform specializes in bridging the “last mile” of AI adoption, unlocking AI's full potential while driving efficiency and significant cost savings. Trase is at the forefront of AI Agent innovation, topping the Hugging Face GAIA Leaderboard for Generalized AI Assistants, ahead of industry giants such as Google, Meta, Microsoft, and OpenAI. We are leveraging our cutting-edge technologies to develop mission-critical agentic applications in complex industries such as Healthcare, Oil & Gas, and National Security. 

About The Role

As Principal Software Engineer, you’ll own the core execution model and platform architecture of Trase OS - the shared platform (“agentic operating system”) that powers all Trase deployments in regulated environments. You’ll define the abstractions and APIs that connect workflows, agents, tools, and product surfaces, and ensure the correctness, scalability, and extensibility of the system.

This is a company-critical role: you are responsible for how the system behaves under real-world conditions, including failure, scale, and security constraints. Your work sets the technical direction for the platform and acts as a force multiplier across all engineering teams.

Clean abstractions and correctness-under-failure are critical because we operate long-lived agents in healthcare/defense environments where auditability and reliability are non-negotiable.

Why This Role Is Needed

Trase OS is an orchestration-heavy system coordinating long-lived workflows, agents, and tools across multiple services and environments.

As the platform evolves, the primary risks shift from implementation to system design quality:

• Poor abstractions create tight coupling across services
• Workflow execution becomes difficult to reason about under failure