Bulgarian Trilingual Language Specialist

About the role

Bulgarians, English speakers, and a third language user enable large-scale language model training that reaches global audiences. Linguists review model behavior so outputs achieve accuracy and naturalness across three languages. This role requires a meticulous approach to evaluating multilingual outputs against rigorous quality standards. The specialist acts as a bridge between raw model behavior and refined, audience-ready language performance. You will systematically examine how models handle subtle linguistic distinctions in Bulgarian, English, and a third language. Your work ensures that translations retain fidelity to the original meaning and context. The position demands a high level of ownership over the linguistic integrity of the evaluation process. Continuous refinement of language-specific guidelines is driven by insights gained from your annotations.

Key facts

What you'll do

Establish evaluation protocols that assess translation clarity and cross-lingual reasoning accuracy across three distinct languages.

Identify and annotate linguistic errors in model responses to improve output clarity and correctness systematically.

Analyze multilingual translations to verify that source meaning is preserved and conveyed with natural fluency.

Collaborate with the team to strengthen evaluation methodologies and reinforce language-specific quality standards.

Update language-specific guidelines based on findings to ensure consistent and high-quality AI communication.

Review model outputs to detect nuanced linguistic issues that could affect user understanding in different languages.

Contribute to the refinement of evaluation criteria that align model behavior with language-specific requirements.

Monitor the performance of translations to support large-scale language model training for diverse global audiences.

Provide detailed annotations that guide adjustments in model outputs to meet clarity and accuracy benchmarks.

Participate in the continuous improvement of evaluation protocols that drive the standardization of translation quality.

Requirements

Fluency in Bulgarian and English plus proficiency in at least one additional language is mandatory for this role.

A secure computer and reliable internet connection are required contractor tools you must supply.

Prior experience in translation, linguistics, content evaluation, or AI training is strongly preferred.

Clear communication skills, attention to detail, and the ability to articulate linguistic nuances across languages are essential for effective annotation work.

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

You must be capable of working independently and following complex instructions for evaluating language outputs.

Strong analytical skills are necessary to assess linguistic accuracy and identify patterns in model behavior.

You must be comfortable handling sensitive and large-scale data with a high level of confidentiality.

The ability to maintain consistency across multiple languages while adhering to strict quality standards is required.

Practical notes

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

High-quality multilingual training data improves model performance for diverse communities. Linguistic expertise helps models handle nuance, idiomatic usage, and cross-lingual reasoning. Clear evaluation protocols standardize translation quality and naturalness across languages. This role influences how AI systems communicate with people worldwide.

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.