AI Engineer

• Improve retrieval and grounding so answers cite the tenant's knowledge base accurately
• Design and enforce guardrails around offers, discounts and claims
• Build evaluation suites that catch regressions before customers see them
• Tune latency and cost across Anthropic and OpenAI models, including bring-your-own-key setups
• Turn knowledge-gap analytics into a product feature founders act on
• Prototype and ship AI features with clients during engagements
• Stay current on model capabilities and separate the useful from the noise

• Strong software engineering fundamentals; this is a production engineering role, not a research role
• Hands-on experience shipping LLM features to real users
• Practical knowledge of retrieval, embeddings, context management and evaluation
• Experience with at least one of the major model APIs in production
• A healthy scepticism and the habit of measuring model behaviour instead of assuming it
• Able to work from our Toronto office part of the week

• Experience with structured outputs, tool use and multi-step agent flows
• Background in payments, commerce or other domains where mistakes cost money
• Familiarity with guardrail and validation patterns for user-facing AI
• Experience running evals in CI

• You treat the model as a component, not a magic box
• You would rather ship a reliable small feature than demo an impressive fragile one
• You test with adversarial inputs because customers will
• You write down what you learn so the team compounds
• You care whether the answer is actually true

1. 1Intro call with an engineer, about 30 minutes
2. 2Short take-home assignment around retrieval and grounding on real docs
3. 3Technical conversation about your assignment and AI features you have shipped
4. 4Conversation with the founders
5. 5Offer

Email careers@clr3.org with a short note about yourself, a link to your GitHub or past work, and a CV if you have one. We read every application and reply to all of them.