AI Language Engineer
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Requirements
• NLP Expertise • Strong understanding of NLP concepts such as intent detection, entity recognition, retrieval-augmented generation, summarization, and prompt engineering. • Experience working with or alongside ML models in production or near-production environments. • Strong analytical thinking with an ability to diagnose model behavior and language patterns. • Excellent command of written language and sensitivity to tone, clarity, and user intent. • Collaboration & Communication • Proven ability to work effectively in cross-functional teams. • Clear verbal and written communication skills to explain NLP decisions, tradeoffs, and recommendations. • Tooling & Technical Comfort • Familiarity with NLP tooling, model evaluation workflows, or AI platforms, with a willingness to learn new tools and frameworks. • Multilingual Proficiency: Strong ability to work with non-English languages (e.g., Spanish, French, German) and understand linguistic nuances across locales. • Multilingual Proficiency: • Prompting, Copywriting & Regex: Strong prompt-writing skills for LLMs, understanding of customer-facing copy best practices, and comfort using regular expressions to support NLP improvements. • Prompting, Copywriting & Regex: • We have noticed a rise in recruiting impersonations across the industry, where scammers attempt to access candidates' personal and financial information through fake interviews and offers. All Cresta recruiting email communications will always come from the @cresta.ai domain. Any outreach claiming to be from Cresta via other sources should be ignored. If you are uncertain whether you have been contacted by an official Cresta employee, reach out to [email protected]
Responsibilities
• AI Language Engineering & Model Work • Design, develop, and refine large language model (LLM) workflows, including context engineering, prompt design, and evaluation frameworks to steer and improve model behaviors. • Build language processing components for features such as intent detection, entity recognition, summarization, retrieval-augmented generation (RAG), and conversational response quality. • Develop speech-to-text (ASR) and text-to-speech (TTS) workflows and evaluation frameworks, bridging audio-feature/signal-level processing with LLM-driven reasoning and orchestration. • Fine-tune and evaluate models using quantitative and qualitative metrics to ensure robust performance across tasks. • Applied NLP & Linguistic Analysis • Analyze model outputs and conversational data to identify patterns, gaps, and failure modes, translating findings into actionable improvements. • Define and apply linguistic evaluation criteria to ensure tone, clarity, intent understanding, and contextual accuracy. • Experiment with prompt structures, retrieval strategies, and linguistic patterns to improve accuracy and robustness. • Data & Experimentation • Drive R&D-style exploration on cutting-edge speech and language systems where best practices are still emerging, rapidly prototyping novel approaches and validating them through rigorous experimentation. • Lead data preprocessing, annotation, and language dataset creation, building reliable training and evaluation corpora. • Design experiments to test model adaptations and new techniques, tracking performance and iterating based on data insights. • Engineering & Product Integration • Collaborate with software developers to integrate language models into production systems and ensure scalable deployment. • Build tooling for model evaluation, monitoring, and continuous improvement pipelines.Extend evaluation and monitoring tooling to support large-scale, automated speech quality measurement for TTS and ASR in offline tests and production. • Support performance optimization, model serving architecture, and infrastructure integration. • Cross-Functional Collaboration • Partner with product managers, conversation designers, UX researchers, and stakeholders to connect language capabilities with business objectives. • Serve as the NLP & language subject-matter expert within multidisciplinary teams. • Documentation & Knowledge Sharing • Document methodologies, evaluation findings, best practices, and language guidelines to promote shared knowledge and reproducible workflows. • Present results and recommendations clearly to internal and external stakeholders.
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