Principal Software Engineer

Standard Template Labs is a stealth-mode, AI-native startup reimagining the future of IT Service and Configuration Management. Backed by leading investors, we're leveraging AI, graph-based architecture, and exceptional design to transform how enterprises manage and engage with their technology ecosystems.

About the Role

We’re looking for a Principal Software Engineer, Full-Stack AI to own the end-to-end technical vision for how intelligence is designed, built, and experienced across our platform - from data ingestion and model reasoning to APIs, user interfaces, and real-world operational impact.

This is a deeply hands-on role for a senior technical leader who thrives at the intersection of AI systems, distributed infrastructure, and product-grade software engineering. You will architect and ship production AI systems, build scalable backend and data platforms, and work across the stack to ensure AI capabilities are observable, trustworthy, and intuitive for enterprise users.

You’ll help us define what “applied, full-stack AI” means at Standard Template Labs: designing reasoning pipelines, operationalizing LLMs and agents, shaping human-in-the-loop experiences, and building the platform primitives that allow intelligence to be embedded - not bolted on - across every workflow. You’ll mentor senior engineers, influence product direction, and help establish an engineering culture where AI, systems design, and user experience are tightly integrated.

• Architect the core intelligence layer of the platform, spanning data ingestion, embeddings, retrieval, graph reasoning, agents, and real-time inference.
• Define how LLMs and predictive models integrate across backend services, APIs, and user-facing experiences.
• Identify high-impact opportunities where generative, predictive, or autonomous AI can eliminate operational toil, improve system understanding, or enhance decision-making.
• Lead architectural decisions around model selection, evaluation, fine-tuning, and inference infrastructure (custom vs OSS vs managed APIs).
• Establish best practices for AI-first engineering, including prompt and schema design, context assembly, evaluators, guardrails, observability, and continuous model monitoring.
• Partner with product and leadership to align AI capabilities with customer outcomes, trust requirements, and long-term platform strategy.

• Build end-to-end AI-powered features - from backend reasoning services to APIs and user-facing workflows.
• Design and implement production-grade LLM and agent workflows, including automated enrichment, anomaly explanation, topology discovery, change impact analysis, and natural language querying.
• Develop scalable backend systems for high-throughput inference, embedding generation, vector search, and graph traversal.
• Collaborate on or directly contribute to frontend experiences that make AI outputs understandable, actionable, and debuggable for users (e.g., explanations, confidence signals, provenance, and feedback loops).
• Implement retrieval-augmented generation (RAG) pipelines and hybrid search systems that combine structured data, graphs, and unstructured context.
• Write clean, well-structured, production-quality code—and champion AI-assisted development tools (Claude, Cursor, Windsurf, etc.) to improve velocity and correctness.
• Continuously evaluate emerging AI frameworks, agent runtimes, orchestration tools, and model APIs, integrating them where they drive real user value.

• Design data models and pipelines that support learning, reasoning, and traceability across the platform.
• Build and evolve distributed systems that are observable, fault-tolerant,…