Prompt Engineer

Job Description

We are looking for a Prompt Engineer with a rare blend of linguistic precision, computational intuition, and a deep understanding of large language models (LLMs). This role isn’t about casually writing prompts — it’s about architecting instructions that turn probabilistic models into structured, predictable tools. You’ll be embedded in the core AI engineering team, collaborating with research scientists, product designers, and dev teams to build reusable prompt frameworks that can be leveraged across applications like AI tutors, agents, copilots, and decision tools. You should be comfortable reverse-engineering prompt failures, designing test suites to validate model behaviors, and iterating on prompt stacks with traceable improvements.

This role suits someone who’s obsessed with language, systems thinking, and getting LLMs to behave as intended — even under edge cases.

Key Responsibilities:

• Design, test, and refine complex prompt structures that drive consistent, high-accuracy LLM outputs in production.
• Build modular prompt systems using techniques such as chain-of-thought, self-reflection, few-shot tuning, and dynamic instruction injection.
• Collaborate with research scientists to bridge the gap between model capabilities and real-world applications.
• Define evaluation metrics and create benchmark suites to validate prompt performance across scenarios.
• Develop internal documentation, style guides, and reusable prompt libraries tailored to different task types.
• Diagnose and troubleshoot model behavior with tracing tools and log inspection.
• Stay updated with the latest research on prompting techniques, jailbreak vulnerabilities, and alignment strategies.

Required Qualifications:

• Proven experience designing and testing prompts for LLMs in a product or research setting (e.g., GPT-4, Claude, PaLM).
• Strong command of language: background in linguistics, logic, philosophy, technical writing, or related fields.
• Comfort with code: working knowledge of Python, JSON, and prompt chaining APIs.
• Analytical mindset: familiarity with experiment design, A/B testing, and evaluation metrics specific to LLM behavior.
• Exceptional communication skills: ability to explain prompt logic clearly to non-technical collaborators.
• Demonstrated understanding of token economy, model context windows, and how instruction weight influences outputs.
• Experience with vector databases, embeddings, and retrieval-augmented generation (RAG) is a strong plus.
• Knowledge of prompt attacks, red-teaming, and safety challenges in LLM deployments is advantageous.

Preferred Experience:

• Prior work in human-computer interaction, computational linguistics, or AI safety.
• Published or internal documentation showing systematic prompt optimization and measurable outcomes.
• Contributions to open-source prompt libraries or AI tooling frameworks.

Are you interested in this position?

Apply by clicking on the “Apply Now” button below!

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