ML Engineer – NLP

This is a fully remote position, open to applicants in Poland.

📋 Description

• Establish priority Polish-language use cases and quality benchmarks in collaboration with Product and go-to-market teams.

• Assess existing model quality in Polish and analyze errors related to language usage, terminology, and user tasks.

• Create Polish-language datasets and evaluations, utilizing native-speaker reviews as necessary.

• Enhance model quality through improved data, fine-tuning, post-training, or other adaptation techniques.

• Implement regression tests, quality assessments, and monitoring for updates on Polish-language models.

• Leverage evaluation findings and market insights to pinpoint remaining gaps and propose subsequent actions.

⛳️ Requirements

• A minimum of 3 years of practical experience in ML or NLP engineering, including involvement with a non-English or multilingual production system.

• Proficient in Python programming.

• Familiarity with PyTorch, JAX, TensorFlow, or a comparable ML framework.

• Experience in developing evaluations that mirror real user tasks, integrating automatic metrics with human assessments.

• Skilled in preparing and validating language datasets while constructing dependable data pipelines.

• Direct experience in fine-tuning, post-training, or adapting NLP or LLM models and measuring their performance.

• Strong software engineering capabilities and production ML skills, encompassing model versioning, regression testing, and monitoring.

• Professional working proficiency or native-level fluency in Polish is preferred.

• Experience in enhancing an AI or NLP product for a new language or market is beneficial.

• Involvement in research or open-source projects in multilingual NLP or model evaluation is a plus.

🏝️ Benefits

• Competitive compensation package in line with talent benchmarks.

• Engaging work that shapes AI capabilities for Kiefer Tech’s products and their real-world applications.

• An AI-native environment offering challenges in model development, evaluation, and production AI systems.

• Engineering-focused culture.

• High level of autonomy.

• Minimal bureaucracy.

• Opportunities to contribute to meaningful AI products.