Engagement Model: Full-time Employment
Estimated Start Date: May 2025
DataForce by TransPerfect is seeking a Machine Learning Engineer to join our team in the Barcelona, Spain.
Role Summary
As a Machine Learning Engineer to help us build and apply ML solutions that scale feature development, utterance understanding, and natural language generation across multiple languages. You will work on cutting-edge technology at the intersection of multilingualism and agentic LLMs. This role requires hands-on development, collaboration across teams, and a deep understanding of large-scale ML systems.
Role Responsibilities
- Work on state-of-the-art solutions involving agentic LLMs and multilingual learning.
- Understand and influence how large language models (LLMs) acquire capabilities across different languages.
- Design and implement systems to automate multilingual data creation and filtering.
- Develop scalable methods to fine-tune LLMs in multiple languages.
- Create automated evaluation systems for multilingual agent performance.
- Contribute to and enhance existing ML systems and infrastructure.
- Build and deploy ML-powered products that serve global users.
- Coordinate across multiple engineering and research teams to ensure alignment and product quality.
Job requirements
- Master’s or PhD in Machine Learning, Computational Linguistics, or equivalent experience.
- 2+ years of experience in software engineering or applied machine learning.
- Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow).
- Experience with large-scale ML systems, LLMs, or NLP architectures.
- Proven ability to ship ML-based products with measurable impact.
- Strong understanding of multilingual datasets and challenges in cross-lingual modeling.
- Excellent communication skills and experience working across teams.
- Idiomatic fluency in English.
- Experience working with one of the following languages: Danish, Swedish, Dutch, Vietnamese, Turkish, Chinese, Portuguese, Arabic, Finnish, Hebrew, Hindi, Malay, or Thai.
Preferred Skills:
- Experience with agent-based architectures, LLM fine-tuning, and evaluation.
- Background in multilingual or cross-lingual natural language processing.
- Experience building tools for automated data generation, augmentation, or filtering.
- Familiarity with evaluation metrics for LLMs and conversational agents.
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