This free Prompt 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 someone to design, test, and optimize the prompts and evaluations that drive your LLM-powered features. It's a newer role that blends language, product sense, and a systematic, experimental approach.
Prompt engineering candidates want to know how the role fits with engineering and product, and how much it involves evaluation and tooling versus pure prompt writing. Be specific, since the role's depth varies widely.
If the role is really full software engineering with AI, use the AI Engineer template instead; if it's research, use the Research Scientist template.
Writing tips
- Clarify how the role works with AI engineers, product, and data.
- Emphasize systematic evaluation, not just writing clever prompts.
- Describe the features and models they'll work on.
- Be clear about how technical the role is (tooling, code, data).
- 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]. LLMs power [feature/area], and we're hiring a Prompt Engineer to make them reliable, accurate, and genuinely useful.
The role
As a Prompt Engineer, you'll design and refine the prompts, context, and evaluations behind our AI features. You'll run experiments, measure quality rigorously, and partner with engineers and product to ship LLM experiences that actually work. This role reports to {{hiring_manager}} and is based {{work_type}} in {{location}}.
What you'll do
- Design, test, and iterate on prompts and context for our AI features.
- Build and run evaluations to measure output quality systematically.
- Diagnose failure modes and improve reliability and accuracy.
- Partner with AI engineers to ship prompt changes safely.
- Keep up with prompting techniques and model capabilities.
What we're looking for
- Hands-on experience designing prompts for production LLM features.
- A systematic, experimental approach to evaluation and iteration.
- Strong language skills and an eye for nuance and edge cases.
- Comfort with data and ideally some scripting ([Python]).
- Curiosity about a fast-moving field.
Nice to have
- Experience building eval frameworks or using eval tooling.
- A background in linguistics, software, or data.
- Familiarity with RAG and agent workflows.
What we offer
- Salary range: {{salary_range}}, plus equity.
- [Comprehensive benefits].
- Flexible {{work_type}} working and [PTO policy].
- A direct hand in the quality of our AI features.
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 a Prompt Engineer do?
- A Prompt Engineer designs, tests, and optimizes the prompts, context, and evaluations that drive LLM-powered features. They run experiments, measure output quality systematically, diagnose failure modes, and work with engineers to ship reliable, accurate AI experiences.
- Is prompt engineering a real job?
- Yes, though the scope varies. At some companies it's a standalone role focused on prompts and evaluations; at others it's folded into AI or ML engineering. The enduring value is systematic evaluation and iteration on LLM behavior, not just writing clever one-off prompts.
- What skills should a Prompt Engineer have?
- Hands-on experience designing prompts for production features, a systematic and experimental approach to evaluation, strong language skills and attention to edge cases, and comfort with data and light scripting. Curiosity about a rapidly changing field is essential.