Prompt Engineer

Job Description

We’re looking for a Prompt Engineer who thrives at the intersection of language, logic, and systems. This is not a generic “write a few prompts” role — you’ll be building, testing, and iterating on large-scale prompt pipelines used in production AI products. You’ll collaborate with research scientists, ML engineers, and product designers to refine behavior, extract latent capabilities, and maximize controllability of large language models (LLMs). Success in this role means building prompts that scale across millions of users without losing nuance or accuracy.

You’ll need a sharp eye for unintended outputs, a deep understanding of instruction tuning, and the curiosity to explore edge cases. If you’ve ever reverse-engineered a prompt to debug its reasoning path, you’ll feel right at home.

Key Responsibilities:

• Design prompt architectures for tasks such as multi-step reasoning, code generation, and contextual understanding.
• Analyze and deconstruct model failures; revise prompt strategies to reduce hallucinations, ambiguity, and drift.
• Conduct prompt ablation studies and track performance metrics across use cases.
• Partner with fine-tuning teams to inform data collection and post-processing workflows.
• Build prompt chaining systems and templates for dynamic use in deployed applications.
• Develop evaluation scripts and prompt validation tools to detect regressions.
• Act as a domain expert for language-based interfaces in human-AI interaction loops.

Qualifications:

Required:

• 3+ years experience working with LLMs, including OpenAI, Claude, Gemini, or open-source equivalents.
• Demonstrated ability to design and optimize complex prompt strategies (e.g., few-shot, chain-of-thought, scratchpad formats).
• Proficiency in Python and tools for interacting with LLM APIs (e.g., LangChain, OpenAI SDK, PromptLayer).
• Strong intuition around language ambiguity, cognitive bias, and emergent behavior in language models.
• Portfolio or documented experiments showing prompt iteration and evaluation outcomes.
• Ability to distill abstract behavior into precise, reproducible tests.

Preferred:

• Experience with RAG systems or prompt-instructed retrieval pipelines.
• Background in computational linguistics, philosophy of language, or related field.
• Familiarity with tokenization patterns (e.g., tiktoken) and latency-performance tradeoffs in prompt length.
• Prior work on safety, alignment, or adversarial prompting is a major plus.

Nice to Have:

• Experience contributing to prompt libraries, prompt tooling frameworks, or internal LLM sandboxes.
• Familiarity with model alignment strategies and responsible AI protocols.

Are you interested in this position?

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

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