This free LLM Engineer job description template is ready to use — copy it, replace the {{placeholders}}, and post your role in minutes. It includes a company intro, a role summary, responsibilities, requirements, nice-to-haves, and compensation, with writing tips and FAQs below to help you tailor it to your team.
When to use this template
Use this when you're hiring an engineer who works deep on large language models — fine-tuning, building retrieval and serving infrastructure, and optimizing models for quality, latency, and cost. It's more specialized than a general AI Engineer who mainly calls APIs.
LLM engineering candidates want to know whether you fine-tune or mostly use hosted models, your scale, and your infrastructure. Be specific, because there's a big difference between prompting hosted APIs and training and serving your own models.
If the role is mainly building features on top of model APIs, use the AI Engineer template; if it's pure research, use the Research Scientist template.
Writing tips
- Clarify whether you fine-tune and serve your own models or mostly use hosted APIs.
- Describe your scale and infrastructure for training and inference.
- Distinguish from the more application-focused AI Engineer role.
- Emphasize evaluation, latency, and cost alongside model quality.
- Include the salary range and seniority level.
The job description
Copy the template below and replace the {{placeholders}} and [bracketed notes] with your specifics.
About {{company}}
{{company}} is [what you do]. Large language models are core to [feature/area], and we're hiring an LLM Engineer to push their quality, speed, and cost in the right direction.
The role
As an LLM Engineer, you'll work deep on the model layer — fine-tuning, building retrieval and serving infrastructure, and optimizing LLMs for quality, latency, and cost. You'll partner with AI engineers and scientists to ship models that hold up in production. This role reports to {{hiring_manager}} and is based {{work_type}} in {{location}}.
What you'll do
- Fine-tune and adapt language models for our use cases.
- Build retrieval (RAG) and serving infrastructure for LLMs.
- Optimize models for latency, throughput, and cost.
- Build evaluations to measure and improve model quality.
- Partner with AI engineers and scientists across the stack.
What we're looking for
- 3+ years in ML or AI engineering, with hands-on LLM experience.
- Strong Python and experience with [PyTorch / Hugging Face / your stack].
- Practical understanding of fine-tuning, RAG, and evaluation.
- Experience serving and optimizing models in production.
- A focus on reliability, latency, and cost.
Nice to have
- Experience with distributed training or inference optimization.
- Familiarity with vector databases and embeddings at scale.
- Contributions to open-source LLM tooling.
What we offer
- Salary range: {{salary_range}}, plus equity.
- [Comprehensive benefits].
- Flexible {{work_type}} working and [PTO policy].
- Deep, frontier work on the models behind our product.
How to personalize
Replace these placeholders before posting:
- {{company}}
- {{location}}
- {{work_type}}
- {{salary_range}}
- {{hiring_manager}}
The bracketed notes — like [your benefits] or [your primary language(s)] — are prompts to swap in your own details. The more specific you are about the actual work and stack, the stronger your applicant pool will be.
Frequently asked questions
- What does an LLM Engineer do?
- An LLM Engineer works deep on the large language model layer — fine-tuning and adapting models, building retrieval (RAG) and serving infrastructure, and optimizing for quality, latency, and cost. They focus on the models themselves rather than only calling hosted APIs.
- What's the difference between an LLM Engineer and an AI Engineer?
- An AI Engineer builds product features on top of foundation models, mostly via APIs and prompting. An LLM Engineer works deeper on the model layer — fine-tuning, serving, and optimizing models. AI engineering is application-focused; LLM engineering is model- and infrastructure-focused.
- What skills should an LLM Engineer have?
- Strong Python and ML engineering skills, hands-on experience with fine-tuning, RAG, and evaluation, and experience serving and optimizing models in production. Familiarity with frameworks like PyTorch and Hugging Face and a focus on latency and cost are key.