Job description

STLabs is an AI service management platform that resolves employee requests end-to-end, grounded in a living model of the enterprise. This matters because we're able to resolve requests with full context - the people, systems, services, and policies that define how an organization actually runs. Requests that used to take days of back-and-forth resolve in minutes, right where employees already work. 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. RESPONSIBILITIES AI-NATIVE ARCHITECTURE & TECHNICAL STRATEGY - 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. FULL-STACK APPLIED AI DEVELOPMENT - 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. DATA, INFRASTRUCTURE & PLATFORM FOUNDATIONS - Design data models and pipelines that support learning, reasoning, and traceability across the platform. - Build and evolve distributed systems that are observable, fault-tolerant, and cost-efficient under AI workloads. - Partner with infrastructure and DevOps teams to shape deployment, scaling, monitoring, and rollback strategies for AI-driven services. - Ensure AI systems meet enterprise requirements for reliability, security, explainability, and compliance. MENTORSHIP, INFLUENCE & TECHNICAL LEADERSHIP - Mentor engineers on full-stack AI patterns, system design for AI workloads, and practical approaches to shipping intelligent features. - Lead architecture reviews and technical deep-dives focused on reliability, safety, performance, and user trust. - Influence engineering standards and culture, emphasizing craftsmanship, clarity, and ownership across the stack. - Help attract and develop top-tier engineering talent excited about AI-native, product-driven systems. QUALIFICATIONS - 10+ years of professional software engineering experience, including technical leadership in complex, high-scale systems. - Proven experience architecting and shipping distributed systems with meaningful AI, automation, or intelligent decisioning components. - Hands-on experience with LLMs, embeddings, vector databases, RAG pipelines, agent frameworks, or model integration patterns. - Strong system design skills across APIs, data modeling, event-driven architectures, caching, storage, and performance optimization. - Comfort working across the stack, including backend services and collaboration on user-facing or API-layer design. - Proficiency in at least one modern programming language (Go, Rust, Python, Java, or C++). - Experience mentoring senior engineers and driving engineering best practices. - Familiarity with AI-assisted development workflows and modern DevOps/tooling. NICE TO HAVE - Experience operationalizing ML or LLM workloads in production at scale. - Background in microservices, event-driven systems, or real-time data pipelines. - Exposure to frontend frameworks or strong product intuition around AI UX. - Experience with high-throughput, low-latency, or mission-critical systems. - Open-source contributions or demonstrated technical leadership in distributed systems or AI tooling. WHY THIS ROLE This is a rare opportunity to define and build the foundations of a truly AI-native, full-stack enterprise platform—one where intelligence spans data, infrastructure, and user experience, and where reasoning systems are core to how customers understand and operate their technology. You won’t just integrate models - you’ll shape how humans and intelligent systems collaborate at scale. WHAT WE OFFER - The opportunity to architect foundational systems for an AI-first enterprise platform. - Ownership over critical, high-impact systems that scale to millions of users. - A culture that values craftsmanship, autonomy, and technical excellence. - Competitive compensation, equity, and a comprehensive benefits package. As an equal opportunity employer, we don’t tolerate discrimination or harassment of any kind. Whether that’s based on race, ethnicity, age, gender identity, citizenship, religion, sexual orientation, disability, pregnancy, veteran status or any other protected characteristic as outlined by federal, state or local laws. The reasonably estimated yearly salary for this role at is: $200,000—$250,000 USD.

Requirements

Must-haves

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, and cost-efficient under AI workloads.
Partner with infrastructure and DevOps teams to shape deployment, scaling, monitoring, and rollback strategies for AI-driven services.
Ensure AI systems meet enterprise requirements for reliability, security, explainability, and compliance.
Mentor engineers on full-stack AI patterns, system design for AI workloads, and practical approaches to shipping intelligent features.
Lead architecture reviews and technical deep-dives focused on reliability, safety, performance, and user trust.

Nice-to-haves

Experience operationalizing ML or LLM workloads in production at scale.
Background in microservices, event-driven systems, or real-time data pipelines.
Exposure to frontend frameworks or strong product intuition around AI UX.
Experience with high-throughput, low-latency, or mission-critical systems.
Open-source contributions or demonstrated technical leadership in distributed systems or AI tooling.

Application prep

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