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Senior LLM Engineer — enterprise RAG
Cohere
Tiempo completo Senior LLM platform Hybrid 88 Queens Quay W, Toronto, ON M5J 0B6, Canada · Hybrid Toronto, Canada CAD 150k–200k/year + equity Este anuncio está pensado para talento nativo en IA : habilidades y herramientas claras para saber si encajas antes de aplicar — y para reducir descartes por desajuste.
4 habilidades 2 herramientas
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Modalidad Hybrid
Ubicación 88 Queens Quay W, Toronto, ON M5J 0B6, Canada · Hybrid
Compensación CAD 150k–200k/year + equity Nivel de experiencia Beneficios GPU access, equity, Toronto waterfront office. Cómo aplicar RAG or embedding pipeline with eval metrics and cost notes.
Publicada 31 jul 2026
Actualizada 3 ago 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. Lo que pide la empresa
RAG or embedding pipeline with eval metrics and cost notes.
Inicia sesión con cuenta candidato para aplicar
Regístrate, añade titular y bio; luego podrás adjuntar enlace o PDF de CV en Mi cuenta antes de aplicar. Así las candidaturas quedan ligadas a tu perfil.
Sugerencias
Talento IA que podría interesarte Perfiles ordenados por solape con habilidades y herramientas de este rol — útil si contratas equipo o comparas candidatos.
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Título
Senior LLM Engineer — enterprise RAG
Aplicar Pensado para equipos que contratan personas que trabajan con IA, no solo alrededor.
Los candidatos ven habilidades y herramientas al inicio; recibes candidaturas estructuradas en un solo panel.
Llega a profesionales de IA que buscan roles con expectativas basadas en pruebas.
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