Bulgarian Language Specialist
Job description
About the role
The owns the detailed linguistic evaluation of AI model outputs to ensure they meet the highest standards of quality for Bulgarian speakers. You will drive the analysis of model reasoning by producing documented error traces that reveal weaknesses in linguistic understanding and harden model performance. A core part of your ownership involves verifying factual accuracy and logical soundness through carefully designed language scenarios specific to Bulgarian tasks. You will shape future AI capabilities by defining evaluation metrics and evidence-driven improvements to prompt engineering for the Bulgarian language. This role centers on detailed linguistic analysis that directly influences how models process and generate Bulgarian content. You will establish the standards for high-quality training data that strengthen model reasoning for Bulgarian speakers. Your work will define the scope and direction of AI capabilities for Bulgarian language tasks within the organization.
Key facts
What you'll do
You will analyze each interaction to produce documented error traces that expose failure modes and reveal weaknesses in linguistic understanding to harden model reasoning. You will verify factual accuracy and logical soundness through language scenarios that test how models handle Bulgarian tasks and identify subtle inconsistencies. You will refine prompt engineering and evaluation metrics based on evidence-driven analysis to improve how models process Bulgarian language tasks. You will assess model outputs against strict criteria for Bulgarian grammar, syntax, morphology, spelling, phonology, and pragmatics. You will translate complex linguistic observations into clear reports that guide data scientists and engineers in improving model behavior. You will collaborate with cross-functional teams to align evaluation frameworks with business objectives and technical constraints. You will maintain detailed records of linguistic patterns and anomalies to support long-term improvements in model performance. You will contribute to the development of best practices for Bulgarian language evaluation in data-driven environments.
Requirements
The posting states a pay range of $8 to $65.
A master's or PhD in Bulgarian language, linguistics, or a closely related field is required for this role. Clear, metacognitive communication that shows your work is essential for tracking model behavior and decisions in linguistic analysis. Demonstrated expertise in Bulgarian grammar, syntax, morphology, spelling, phonology, and pragmatics must be established through peer-reviewed publications, teaching experience, or hands-on linguistic analysis projects aligned with the role. You must possess strong analytical skills and the ability to break down complex linguistic phenomena into structured, reproducible assessments. You should be comfortable working with technical documentation and collaborating with data scientists and engineers. You must have the ability to work independently with minimal supervision while maintaining high standards of accuracy. You need to manage multiple tasks and priorities in a contract-based environment while adhering to strict quality standards.
Nice to have
The source does not specify any preferred or nice-to-have qualifications beyond the core requirements.
Practical notes
This contractor role requires a secure computer and high-speed internet that you must supply.
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
Questions to ask
Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.
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.