Linguistic Engineer

Meta•Burlingame, CA

5d•$138,000 - $189,000

About The Position

Meta's Reality Labs Wearables division is developing future augmented and virtual reality products. The Linguistic Engineer helps deliver datasets, models, knowledge, and data infrastructure that power machine-learning systems across components of the multimodal voice assistant, including ASR/TTS, NLU, NLG, Dialog, LLMs, and Knowledge Graph. The role develops datasets and consistent models and representations across languages and areas, and supports the voice assistant product in collaboration with machine-learning teams. The ideal candidate brings technical, analytical, and collaboration skills, experience building datasets for machine-learning applications, and experience with data tools, pipelines, and analytics. Experience as an ML practitioner is useful but not required. Multilingual datasets or NLP systems experience and an interest in NLP or conversational AI are relevant; computational linguistics experience is a plus but not required.

Requirements

Experience as a data scientist, software engineer, computational linguist, or in a similar role.
Experience with programming and data analysis using languages and platforms such as Python, SQL, and PHP/Hack.
Experience with text analysis, scripting, relational databases, NoSQL databases, or similar.
Experience shipping multiple products across various platforms.
A degree in Linguistics, Computational Linguistics, Computer Science, Data Science, Information Systems, or a related field, or equivalent experience.

Nice To Haves

Experience adhering to and implementing responsible, ethical AI practices, such as risk assessment, bias mitigation, and quality and accuracy reviews.
Fluency in additional languages, given the role's work building and evaluating multilingual datasets and models for the voice assistant product.
Experience designing and conducting data experiments.
Advanced coursework and/or research in Linguistics, Computer Science, Data Science, Computational Linguistics, Information Systems, or related fields.
Ability to integrate AI tools to optimize or redesign workflows and drive measurable impact, such as efficiency gains or quality improvements.
Experience with version control, unit tests, and other programming best practices.
Experience with larger scripting projects that combine language data from different sources or compute complex metrics over large datasets.
Ongoing AI skill development, such as prompt/context engineering or agent orchestration, and staying current with emerging AI technologies.
Practical knowledge of the relationship between data and machine-learning models.

Responsibilities

Build datasets, pipelines, and models for ML applications.
Directly support product development with rules, prompts, and data patches.
Evaluate model quality and product experiences, and close the feedback loop.
Communicate clearly with project stakeholders.
Identify and document repeatable practices for data quality, pipeline reliability, and model evaluation, and improve procedures across data systems.
Drive and deliver projects from conceptualization through launch and beyond, with continual improvement and support.
Design and conduct product experiments.
Identify and scope undefined problems, propose structured approaches, and deliver solutions with measurable outcomes.
Manage and prioritize concurrent work streams based on impact, deadlines, and stakeholder needs.
Collaborate with cross-functional teams, including product, engineering, and data science.