Basque Trilingual Language Specialist

About the role

This role directs language evaluation and annotation that shapes multilingual AI training. Linguistic expertise and cultural awareness guide how Basque, English, and a third language are handled to improve model outputs. The position advances inclusive language practices and output quality for global users.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Language errors are annotated and corrected so evaluation protocols and language-specific guidelines grow stronger over time. Such updates raise consistency and accuracy for the specified language pairs in real-world use.

Multilingual translations are reviewed for clarity and fidelity against source content, preserving naturalness across language directions. Meaning and tone are checked to keep messages intact for varied audiences.

Evaluation methods are shaped through team collaboration, using observed patterns in model behavior to update language-specific guidelines. Shared standards define how model outputs are judged for every supported language.

Stable tools and access support steady contribution and timely delivery of high-quality annotations.

Requirements

The posting states a pay range of $6 to $65.

Fluency in Basque and English, plus proficiency in at least one additional language, is required to cover all evaluation scenarios. Prior experience in translation, linguistics, content evaluation, or AI training is strongly preferred and matches the expected responsibilities.

Clear communication skills, attention to detail, and the ability to express linguistic nuances across languages ensure annotations reflect subtle meaning differences and register. These abilities keep outputs accurate and context-appropriate.

Practical notes

The role is remote with no specified onsite days, allowing flexible participation from any location. to finalize your understanding of the terms.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Work in this field involves evaluating and refining language outputs for AI systems across multiple languages. Success depends on strong judgment around grammar, idiom, and cultural context in diverse settings. The work uses standard evaluation tools and structured guidelines to maintain consistency. The field emphasizes accuracy, clarity, and inclusive language practices in every project.

Questions to ask

Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.