Senior Language Engineer

About the role

The owns the linguistic integrity of the voice AI stack from data creation to production evaluation. You will architect data pipelines that transform raw web-scale interactions into high-quality training and evaluation datasets for voice agents. This role requires sharp linguistic judgment to make opinionated decisions about what sounds natural, conversational, and humanlike in enterprise call center scenarios. You will coordinate a human annotation workforce to execute against strict quality targets while maintaining alignment with product requirements. You will partner directly with researchers and ML engineers to turn linguistic insights into preference data that drives each modeling iteration. The position sits at the frontier of applied language technology where data quality directly determines voice AI performance at scale. You will play a critical role in ensuring our AI workers sound indistinguishable from expert human agents to customers like CVS/Aetna, American Airlines, Lenovo, and Grab.

Key facts

What you'll do

• Define requirements and own the end-to-end pipeline for creating high-quality datasets for voice agent use cases, across both text and audio modalities.
• Engineer web-scale data pipelines and apply synthetic generation techniques to produce high-quality training and evaluation data at production volume.
• Coordinate and manage a human annotation workforce: author guidelines, define quality targets, and QA annotator output to ensure consistent standards.
• Build data processing and cleaning pipelines that align datasets to production needs, balancing coverage across use cases, languages, and domains for comprehensive voice AI training.
• Analyze production logs, curated datasets, and other sources to surface failure patterns and identify high-leap areas for targeted data collection and improvement.
• Apply your linguistic taste to judge which outputs are more natural, conversational, and humanlike, and produce preference data that encodes that judgment for model optimization.
• Partner with researchers and engineers to drive each modeling iteration, translating linguistic requirements into technical specifications that data teams can implement.
• Evaluate dataset quality against objective metrics and subjective linguistic criteria, iterating on data generation strategies to close identified gaps.
• Design experiments to measure the impact of data interventions on downstream voice AI performance in real customer interactions.
• Maintain detailed documentation of data provenance, processing steps, and quality checks to support reproducibility and auditability.
• Collaborate with product managers to prioritize data initiatives that unblock modeling milestones and improve customer experience metrics.
• Implement robust data validation frameworks that catch errors early and prevent problematic data from propagating into training or evaluation sets.
• Monitor industry benchmarks and internal analytics to inform data strategies that keep Retell AI models competitive in the voice AI market.
• Lead best practices for data security, privacy, and compliance as they apply to voice recordings, transcripts, and annotation workflows.
• Mentor junior data engineers and linguists on effective techniques for scaling high-quality voice data operations.

Requirements

• 2+ years of experience in computational linguistics, language data processing, or a similar field, including hands-on work with large-scale text and audio datasets.
• Highly technical: fluent at writing scripts for data processing and at leveraging models for synthetic data generation in production environments.
• Native-level command of English, with the confidence to make opinionated linguistic calls about what sounds natural in voice agent conversations across diverse use cases.
• 2+ years of experience managing human annotation and evaluation teams, with a track record of maintaining high quality standards under fast-paced conditions.
• Demonstrated ability to translate linguistic expertise into data requirements that engineering teams can implement without ambiguity.
• Experience working with audio data pipelines, including transcription, speech recognition evaluation, and audio quality assessment for voice AI applications.
• Strong understanding of evaluation methodologies for language models, including preference modeling, scoring frameworks, and offline testing strategies.
• Comfort operating in ambiguous, fast-moving startup environments where priorities shift and data-driven decisions are essential.

Nice to have

• A strong applied ML background in language or audio modeling - ideally having contributed to the data pipelines behind a well-known audio or language model in production.
• A PhD in Computational Linguistics or an equivalent field with a computational emphasis and peer-reviewed research in language or speech processing.
• Prior experience at a voice AI, speech recognition, or conversational AI company where data quality directly impacted product outcomes.
• Track record of building scalable data systems that bridge research prototypes and production services in high-stakes customer interactions.

Practical notes

• Location details: Redwood City, CA, US, San Francisco Bay Area.
• US Visas: Retell AI is open to sponsoring work authorization for qualified candidates, including H1B/H-1B, TN, L-1, E-3, F-1 (OPT/CPT).
• Compensation band: Cash 200k - 290k, Equity Provided.
• Time commitment: Full-time engagement required.
• No additional travel requirements are specified beyond standard company operations.