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Senior LLM Engineer — Gemini platform
Google
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 1600 Amphitheatre Pkwy, Mountain View, CA 94043, United States · Hybrid
Compensación $220k–$320k/year + RSUs Nivel de experiencia Beneficios RSUs, hybrid 3/2, on-campus perks. Cómo aplicar Production LLM serving, eval frameworks, or agent tooling at scale.
Publicada 14 dic 2026 Google is hiring a Senior Senior LLM Engineer — Gemini platform on the DeepMind Applied team (Hybrid · Mountain View, United States). 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.
Google builds Gemini , Search, and Cloud AI from Mountain View — home of the original Googleplex and DeepMind applied teams.
Senior LLM Engineer (hybrid Mountain View): model serving , eval infrastructure , and agent frameworks used by billions.
About Google
Mountain View HQ hosts core AI research and product engineering. Strict privacy, safety, and responsible-AI review processes. Cross-functional work with Cloud and Android surfaces. What you will do
Scale LLM inference with SLO-driven autoscaling. Build eval harnesses for multilingual and multimodal quality. Ship developer APIs with backward-compatible versioning. Stack & environment
Python, JAX/PyTorch, LangChain, Borg/Kubernetes, GCP. Hybrid Mountain View 3/2. What we look for
5+ years LLM production systems. Authorized to work in the United States. Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
LangChain
Orchestrating LLM chains, tools, and retrieval with observable steps and failure handling.
TensorFlow
Practical TensorFlow experience applied to LLM features, retrieval, agents, and evaluation in production or rigorous pilots.
Kubernetes Tool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
GCP Tool
Regular use of GCP in the LLM & generative AI stack—configuration, debugging, and team conventions.
✓ You can show evidence in your application: Production LLM serving, eval frameworks, or agent tooling at scale. ✓ Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, LangChain, TensorFlow, and Kubernetes. ✓ 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 · Mountain View, United States)—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: $220k–$320k/year + RSUs, benefits in At a glance) and your expectations align. Lo que pide la empresa
Production LLM serving, eval frameworks, or agent tooling at scale.
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 — Gemini platform
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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