Team & ContextGorgias is an AI-first company building products powered by LLMs and agent-based systems.
As we scale our AI capabilities, we need to improve how we evaluate, iterate, and operate these systems in production. Today, parts of this process remain manual or fragmented, especially around prompt iteration, validation, and evaluation workflows.
This role will focus on
building and scaling the systems that support AI evaluation and iteration, helping the team move faster and more reliably.
About the RoleYou'll have a chance to:
- Work on production AI systems used by thousands of businesses
- Define how we evaluate and improve AI performance at scale
- Build internal platforms and tooling used by AI and engineering teams
- Reduce manual processes and improve iteration speed on AI features
- Collaborate across AI, ML, and product teams
- Raise the engineering bar and mentor others
What You'll Do1. Architect the Evaluation "Factory"- End-to-End Platform Ownership: Architect and lead the development of our internal evaluation platform, moving the needle from manual testing to a fully automated lifecycle (from LLM-as-a-judge creation to production monitoring).
- Accelerate Time-to-Market: Directly impact our primary KPI by designing tools and workflows that drastically reduce the time it takes to deliver a calibrated, production-ready agent.
- Infrastructure Collaboration: Partner with the Orchestration team to build the robust, scalable infrastructure required to run complex evals and agentic simulations at scale.
2. Scaling AI Expertise- Squad Empowerment: Serve as the "AI Technical Lead" for product squads, guiding them through the complexities of agent design, failure analysis, and prompting best practices.
- Decentralize Quality: Instead of being a bottleneck, you will build the "paved road" that allows product squads to become autonomous in measuring and maintaining their own agent quality.
- Standard Setting: Define what "good" looks like for AI at [Company Name]. You'll translate non-deterministic AI behavior into predictable engineering metrics that the whole organization can trust.
3. Engineering Leadership- Mentor & Level Up: Bridge the gap between traditional software engineering and AI. You'll mentor engineers on how to apply rigorous system design to the world of LLMs and agents.
- Continuous Observability: Take ownership of the feedback loop, ensuring that production insights from our agents directly inform the next iteration of our evaluation datasets.
Who You Are- 8+ Years of Engineering Excellence: You are a Staff-level engineer first. You've built systems that handle high scale, and you know how to architect for long-term maintainability and performance.
- Agentic Curiosity: You've moved beyond the "chatbot" phase and are actively experimenting with AI Agents. You understand that the challenge isn't the prompt, but the orchestration, state management, and reliability of the agent's actions.
- Systems Thinker (Non-Deterministic Mindset): You recognize that AI is probabilistic. You are excited by the challenge of building deterministic "wrappers" and Evaluation loops around models to make them safe for production.
- The "Applied" Edge: You likely come from a background in distributed systems, internal platforms, or developer tooling, and you're now applying that rigor to the AI stack.
What We're Looking For- Beyond the Wrapper: You have serious experience moving beyond simple API calls to architecting multi-stage AI orchestrations (agents, chained workflows, or custom runtime logic).
- Orchestration Experience: Even if you aren't an AI researcher, you have experience building complex, multi-step workflows (e.g., temporal systems, state machines, or event-driven architectures) and want to apply this to Agentic loops.
- Reliability Obsession: You understand why "vibes-based" testing doesn't work. You've started exploring or building Eval frameworks to measure how models perform against real-world data.
- Infrastructure Mindset: You are comfortable with the "glue" that makes AI work: vector databases, semantic caching, and API integration with third-party tools.
Tech Stack & Experience- Strong backend experience (Python preferred)
- Experience with distributed systems and event-driven architectures
- Familiarity with tools like Kafka, Pub/Sub, or equivalent
- Experience working with LLMs (prompting, RAG, agents, evaluation workflows)
- Experience building APIs and scalable services
- Understanding of monitoring, observability, and system performance
Hiring Process- Recruiter phone screen
- HM Interview
- System Design Interview
- AI Case Study (take-home, ~1-2 hours)
- Technical Deep Dive of case study
- Final Leadership Interview
Perks & Benefits 5-week vacation (We follow each country's appropriate PTO Laws)
Paid sick leave
Paid parental leave (16 weeks)
MacBook Pro
Private health insurance and retirement pension (RRSP with Gorgias matching up to 4%)
For a smooth onboarding, we invite you to our Toronto office for one week (flights and accommodation handled by Gorgias)
Get up to $900 CAD to set up your workstation at home
Get up to $2,600 CAD of learning material per year (books, courses, training, and individual coaching)
Every quarter, we organize a company-wide summit to discuss where we're going and strengthen social bonds. Once per year we organize offsite team retreats and company retreats!
AI at GorgiasAt Gorgias, AI is a natural extension of how we work and build. Our teams use it every day to research, write, analyze, code, and craft better customer experiences. Everyone has access to premium AI tools (ChatGPT, Claude, Granola & others) and an annual L&D budget to explore new ones.
The real magic happens when we share what we learn. Our #powerup Slack channel is a digital petri dish of new tools and workflows, and each team has AI champions who showcase fresh ideas during weekly company-wide standups, now practically AI demo sessions.
We see AI not as a replacement for creativity or empathy, but as a multiplier, helping us move faster, think deeper, and serve customers better.
