AI role · Open offer
Senior LLM Engineer — Gemini platform
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.
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At a glance
How you'll work, where, and key listing dates.
- Employment
- Full-time
- Workplace
- Hybrid
- Location
- 1600 Amphitheatre Pkwy, Mountain View, CA 94043, United States · Hybrid
- Compensation
- $220k–$320k/year + RSUs
- Experience level
- Senior
- Team
- DeepMind Applied
- Benefits & perks
About this role
What the team is looking for and how this position fits.
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.
Skills we're hiring for
What each capability means for this role—not a keyword checklist.
- 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
Tools & stack
Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).
- KubernetesTool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
- GCPTool
Regular use of GCP in the LLM & generative AI stack—configuration, debugging, and team conventions.
You're a strong fit if…
Tailored to this listing—use it as a self-check before you apply.
- ✓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.
Apply
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What the employer asked for
Production LLM serving, eval frameworks, or agent tooling at scale.
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