Staff Engineer - AI Platform

About High Level:

High Level is an AI-powered business operating system that gives agencies, entrepreneurs and SMBs the infrastructure to build, automate and scale. Today, High Level supports SMBs across 150+ countries, fueling community-driven growth rooted in real customer outcomes. To date, businesses operating on High Level have generated over $7 billion in ecosystem value, demonstrating the impact of shared infrastructure  centralizing conversations, automation and intelligence into one system, we help businesses move faster, reduce complexity and execute efficiently.

Behind the platform, High Level powers more than 4 billion API hits and 2.5 billion message events daily. With 250 terabytes of distributed data, 250+ microservices and over 1 million domain names supported, our architecture is built for performance, resilience and long-term scalability.

Our People

With over 2,000 team members across 10+ countries, High Level operates as a global, remote-first organization built for speed and ownership. We value initiative, clarity and execution, creating space for ambitious people to build systems that support millions of businesses worldwide. Here, innovation thrives, ideas are celebrated and people come first, no matter where they call home.

Our Impact

Every month, High Level enables more than 1.5 billion messages, 200 million leads and 20 million conversations for the more than 1 million businesses we support. Behind those numbers are real people building independence, expanding opportunity and creating measurable impact. We’re proud to be a part of that. Learn more about us on our You Tube Channel or Blog Posts.

We're looking for a Staff Engineer (AI) to architect and build the agentic systems and AI foundations within our Platform Services team - the team responsible for the shared infrastructure, internal tooling, and core services that other product teams build on. This is a deeply hands-on individual contributor role: you will set technical direction, write and review code, and own the design of systems that let High Level engineering teams build, ship, and operate AI-driven capabilities reliably at enterprise scale.

• Production-grade agentic systems - multi-step, tool-using agents with orchestration, planning, memory and context management, and human-in-the-loop controls - for platform automation and internal engineering workflows.
• A shared AI platform for product teams: reusable agent frameworks, tool and MCP integrations, an LLM gateway (routing, rate limiting, caching, fallbacks), and prompt/model versioning. Evaluation and observability for AI systems - eval harnesses, tracing of agent runs, and metrics for quality, latency, cost, and safety.
• AI-driven engineering practices that improve developer velocity - agentic developer workflows, AI-assisted tooling, and automation of operational toil.
• Cloud infrastructure for AI workloads across GCP/AWS, including provisioning, scaling, security, and cost governance for inference and token spend.

• Architecture & Scale: Own and evolve the architecture of agentic systems and AI platform services, ensuring they are reliable, extensible, and performant under enterprise-scale load.
• AI-First Culture: Set and drive an AI-first culture within the Platform Services team - making AI tools and agentic workflows the default way the team builds and operates.
• Reliability & Safety: Build agents that fail safely - with guardrails, scoped permissions, sandboxing, defenses against prompt injection, audit trails, graceful degradation, and clear incident management practices.
• Evaluation & Data-Driven Decisions: Establish rigorous evaluation practices - offline evals, regression suites, and production monitoring - so agent behavior is measurable and comparable across model and prompt changes. Use that data to prioritize, evaluate trade-offs, and report on agent quality, platform health, and cost.
• Cost & Efficiency: Drive continuous optimization of inference, token, and infrastructure cost without compromising quality or reliability - with measurable, sustained impact.
• Technical Leadership & Mentorship: Set technical standards, lead design reviews, mentor engineers, and build a culture of ownership and craftsmanship in AI engineering across the org.
• Cross-Functional Collaboration & Communication: Partner closely with product engineering teams, SRE/infra, security, and leadership to align AI platform capabilities with organizational needs, and…