Localization Linguist

Job Title: Linguist II

Location: Onsite - 3 days in one of preferred offices: Sunnyvale CA, New York NY, Burlingame CA, Washington, or Redmond WA.

Duration: 12 Months

Job Description:
We are looking for a linguist to help develop language components for AI-powered products, including large language models (LLMs) and voice-enabled technologies. We are seeking candidates with solid linguistic data analysis skills, programming familiarity, and language technology experience to contribute to data collection, synthetic data generation, and annotation tasks in support of LLM/AI training, evaluation, alignment, and AI agent development.

Job Responsibilities:
Apply linguistic expertise in syntax, semantics, pragmatics, and sociolinguistics to support LLM and generative AI systems.
Collaborate with linguists, data operations teams, and ML engineers on data collection, curation, annotation, and localization efforts for model training and fine-tuning.
Contribute to the development and maintenance of annotation schemas and guidelines for LLM training data (e.g., instruction-tuning, preference labeling, RLHF).
Evaluate and quality-check datasets used for pre-training, fine-tuning, and alignment of language models.
Support the development of programmatic methods for generating synthetic annotated data at scale.
Assist in model evaluation efforts including prompt-based testing, red-teaming, and linguistic error analysis.
Participate in experiments to assess data quality, annotation consistency, and downstream model performance.

Basic Qualifications:

• Bachelor's degree in Linguistics, Computational Linguistics, Computer Science, Speech Science, or related field.
• 1+ years of experience in Linguistics, Language Technologies, NLP, or AI/ML data operations (or equivalent).
• Knowledge of syntax, semantics, pragmatics, sociolinguistics, corpus linguistics, and other areas of linguistics.
• Familiarity with Large Language Models (LLMs), their applications and data practices (training data, evaluation, prompting, fine-tuning).
• Exposure to LLM evaluation methodologies (human evaluation, automated metrics, adversarial testing).
• Experience working with semantic ontologies, taxonomies, or intent/slot frameworks.
• Proficiency using AI Agents/Chatbots.
• Experience with database queries and data analysis processes (SQL, spreadsheets, R, Unix, or others).
• Experience working with speech and text data in multiple languages.
• Comfortable working in a fast-paced, highly collaborative environment with evolving priorities.

Preferred Qualifications:
Native or near-native fluency in English and at least one additional language.
Master's degree in Linguistics, Computational Linguistics, Language Technologies, or a related field.
Familiarity with machine learning frameworks, NLP libraries, and tools (e.g., Hugging Face, spaCy, NLTK, PyTorch).
Exposure to statistical language modeling or training data pipelines.
Strong organizational skills and attention to detail.

Top 3 Must-have HARD Skills:

• 1+ years of experience in Linguistics, Language Technologies, NLP, or AI/ML data operations (or equivalent).
• Knowledge of syntax, semantics, pragmatics, sociolinguistics, corpus linguistics, and other areas of linguistics.
• Familiarity with Large Language Models (LLMs), their applications and data practices (training data, evaluation, prompting, fine-tuning).
• Exposure to LLM evaluation methodologies (human evaluation, automated metrics, adversarial testing).
• Experience working with semantic ontologies, taxonomies, or intent/slot frameworks.
• Proficiency using AI Agents/Chatbots.
• Experience with database queries and data analysis processes (SQL, spreadsheets, R, Unix, or others).

Good to Have Skills:
Native or near-native fluency in English and at least one additional language.
Master's degree in Linguistics, Computational Linguistics, Language Technologies, or a related field.
Familiarity with machine learning frameworks, NLP libraries, and tools (e.g., Hugging Face, spaCy, NLTK, PyTorch).
Exposure to statistical language modeling or training data pipelines.
Strong organizational skills and attention to detail.
Experience working with speech and text data in multiple languages.
Comfortable working in a fast-paced, highly collaborative environment with evolving priorities.

Education
~ Bachelor's degree required.

Story Behind the Need - Business Group & Key Projects: We are looking for a linguist to help develop language components for AI-powered products, including large language models (LLMs) and voice-enabled technologies. We are seeking candidates with solid linguistic data analysis skills, programming familiarity, and language technology experience to contribute to data collection, synthetic data generation, and annotation tasks in support of LLM/AI training, evaluation, alignment, and AI agent development.

Compelling Story & Candidate Value Proposition:

Job Responsibilities
Apply linguistic expertise in syntax, semantics, pragmatics, and sociolinguistics to support LLM and generative AI systems.
Collaborate with linguists, data operations teams, and ML engineers on data collection, curation, annotation, and localization efforts for model training and fine-tuning.
Contribute to the development and maintenance of annotation schemas and guidelines for LLM training data (e.g., instruction-tuning, preference labeling, RLHF).
Evaluate and quality-check datasets used for pre-training, fine-tuning, and alignment of language models.
Support the development of programmatic methods for generating synthetic annotated data at scale.
Assist in model evaluation efforts including prompt-based testing, red-teaming, and linguistic error analysis.
Participate in experiments to assess data quality, annotation consistency, and downstream model performance.

Typical Day in the Role:

Job Responsibilities:
Apply linguistic expertise in syntax, semantics, pragmatics, and sociolinguistics to support LLM and generative AI systems.
Collaborate with linguists, data operations teams, and ML engineers on data collection, curation, annotation, and localization efforts for model training and fine-tuning.
Contribute to the development and maintenance of annotation schemas and guidelines for LLM training data (e.g., instruction-tuning, preference labeling, RLHF).
Evaluate and quality-check datasets used for pre-training, fine-tuning, and alignment of language models.
Support the development of programmatic methods for generating synthetic annotated data at scale.
Assist in model evaluation efforts including prompt-based testing, red-teaming, and linguistic error analysis.
Participate in experiments to assess data quality, annotation consistency, and downstream model performance.

How Will Performance Be Measured:
~ Contribute to the development and maintenance of annotation schemas and guidelines for LLM training data (e.g., instruction-tuning, preference labeling, RLHF).