Prompt Engineer

Job Description

We are looking for a Prompt Engineer who thrives at the intersection of language, logic, and system design. This role is not about writing endless prompts; it’s about designing precise instructions that exploit and push the capabilities of large language models (LLMs) in production environments. You will collaborate with product managers, researchers, and data scientists to build and refine prompt strategies for everything from chat interfaces and search augmentation to multi-step reasoning workflows and autonomous agents. You will also create evaluation frameworks to measure performance, reduce hallucinations, and improve reliability at scale.

If you think deeply about phrasing, test the limits of LLM behavior, and are obsessive about iteration and testing, this is the role for you.

Key Responsibilities

• Design, prototype, and iterate on complex prompt strategies to optimize LLM performance in real-world applications
• Engineer chained prompting, tool-use instructions, and function calling protocols that support long-horizon reasoning
• Create synthetic datasets and evaluation benchmarks to stress-test model outputs
• Analyze model failures and behavior regressions to refine prompts, formatting, and system instructions
• Collaborate with data labeling teams to guide collection and fine-tuning efforts based on prompt performance
• Work closely with product teams to adapt prompt design to specific domains (legal, healthcare, coding, finance, etc.)
• Contribute to internal libraries for reusable prompt patterns, templates, and evaluation scripts

Qualifications

Required:

• Proven experience working with LLMs (OpenAI, Anthropic, Mistral, etc.) in production or research settings
• Strong command of language and structure; capable of writing clear, concise, and computationally effective instructions
• Familiarity with few-shot, zero-shot, chain-of-thought, and system prompt techniques
• Experience designing and executing experiments on prompt reliability, bias, or task performance
• Strong Python skills and comfort working with tools like LangChain, LlamaIndex, or similar frameworks
• Ability to reason through ambiguity and construct prompts for tasks with no clear solution pattern
• Comfortable reading and analyzing model logits, token probabilities, and response traces

Nice to Have:

• Background in linguistics, philosophy, cognitive science, or logic
• Experience with prompt tuning, fine-tuning, or reinforcement learning from human feedback (RLHF)
• Familiarity with retrieval-augmented generation (RAG) pipelines and embedding-based context injection
• Published work (research papers, blog posts, or tutorials) on prompt engineering, LLM behavior, or related topics

Are you interested in this position?

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

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