AI role · Open offer

Senior LLM Engineer — Gemini platform

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Google

Full-time
Senior
DeepMind Applied
Hybrid
1600 Amphitheatre Pkwy, Mountain View, CA 94043, United States · Hybrid
Mountain View, United States
$220k–$320k/year + RSUs

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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.
LangChain
Orchestrating LLM chains, tools, and retrieval with observable steps and failure handling.
PyTorch

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, LangChain, PyTorch, 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.

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What the employer asked for

Production LLM serving, eval frameworks, or agent tooling at scale.

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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