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Senior LLM Engineer — enterprise RAG
Cohere
Full-time Senior LLM platform Hybrid 88 Queens Quay W, Toronto, ON M5J 0B6, Canada · Hybrid Toronto, Canada CAD 150k–200k/year + equity This listing is optimized for AI-native talent : clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.
Workplace Hybrid
Location 88 Queens Quay W, Toronto, ON M5J 0B6, Canada · Hybrid
Compensation CAD 150k–200k/year + equity Benefits & perks GPU access, equity, Toronto waterfront office. How to apply RAG or embedding pipeline with eval metrics and cost notes.
Listed Jul 31, 2026
Updated Aug 3, 2026 Cohere is hiring a Senior Senior LLM Engineer — enterprise RAG on the LLM platform team (Hybrid · Toronto, Canada). This is a hands-on role in LLM & generative AI —not a generic “AI enthusiast” post.
You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.
Cohere builds enterprise-grade language models and APIs used globally from our Toronto headquarters.
Join as a Senior LLM Engineer to ship RAG, embeddings, and fine-tuning workflows for regulated customers.
What you will do
Ship production LLM and retrieval systems with offline evals, monitoring, and safe rollout. Work with product and domain experts on requirements and trade-offs. Improve data pipelines, labeling, and metrics tied to business KPIs. Document runbooks for on-call and customer-facing teams. What we look for
Strong Python and modern ML/LLM stack with shipped features. SQL, experimentation, and clear communication to stakeholders. Eligible to work in the listed country; professional English required. Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
LangChain
Orchestrating LLM chains, tools, and retrieval with observable steps and failure handling.
OpenAI API
Calling frontier APIs with retries, cost controls, prompt versioning, and safety filters.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
Vector DBs Tool
Embedding storage, hybrid search, and freshness strategies for RAG and semantic retrieval.
Docker Tool
Containerized services and reproducible dev environments for ML and LLM workloads.
✓ You can show evidence in your application: RAG or embedding pipeline with eval metrics and cost notes. ✓ Several of these show up in recent shipped work—not only on your CV: Python, LangChain, OpenAI API, PyTorch, and Vector DBs. ✓ You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals. ✓ You are comfortable with the hybrid rhythm (Hybrid · Toronto, Canada)—onsite collaboration when it matters, deep work when remote. ✓ You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords. ✓ You read the full listing (comp: CAD 150k–200k/year + equity, benefits in At a glance) and your expectations align. What the employer asked for
RAG or embedding pipeline with eval metrics and cost notes.
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Senior LLM Engineer — enterprise RAG
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