Machine Translation Researcher (NMT & LLMs)

Job Title: Machine Translation Researcher (NMT & LLMs) Location: Fully Remote – India Reports To: VP of AI Employment Type: Full-time ________________________________________ About the Role We are looking for a highly experienced

Machine Translation Researcher

to join our team and drive innovation in

Neural Machine Translation (NMT) and LLM-based translation systems . This is a fully remote, long-term position, allowing you to live and work from anywhere while collaborating with a global team. In this role, you will design, train, and deploy state-of-the-art machine translation models, work with large-scale multilingual datasets, and integrate advanced LLM-based translation and RAG pipelines into production workflows. You will also help push the boundaries of translation quality, particularly for low-resource languages and domain-adapted MT systems. This position is ideal for researchers who combine strong theoretical understanding of modern NLP architectures with hands-on experience building and deploying large-scale MT systems. ________________________________________ Key Responsibilities:

Machine Translation Research & Development • Design, train, fine-tune, and evaluate Neural Machine Translation (NMT) models across multiple language pairs (both high- and low-resource languages). • Develop state-of-the-art Transformer-based encoder–decoder models for production translation systems. • Conduct research and experimentation in areas such as: o Domain Adaptation o Knowledge Distillation o Factored MT o Adaptive NMT o Low-resource MT with LLM augmentation • Improve model robustness, generalization, and domain specialization. LLM-Based Translation Systems • Fine-tune and adapt large language models for translation tasks, including: o Qwen o DeepSeek o LLaMA • Apply LoRA / QLoRA and other parameter-efficient fine-tuning methods for multilingual MT tasks. • Build RAG-based translation pipelines using domain-specific knowledge sources. • Design prompting strategies and translation prompting frameworks for LLM-assisted translation workflows. Architecture & Modeling • Design and implement custom Transformer architectures and enhancements such as: o Additional Quality Estimation (QE) heads o Multi-stage LLM chaining pipelines o Hybrid NMT + LLM translation systems • Experiment with scalable architectures for multilingual and low-resource language translation. ________________________________________ Required Qualifications Education • Bachelor’s or Master’s degree in one of the following: Computational Linguistics, Language Technology, Computer Science, Artificial Intelligence or a related field with equivalent experience. Core Experience • Minimum 5 years of research experience in Machine Translation or NLP. • Expert knowledge of Neural Machine Translation architectures, especially Transformer-based encoder–decoder models. • Strong experience in training and evaluating NMT models at scale. • Proven expertise in domain adaptation and fine-tuning of translation models. • Hands-on experience with LLM fine-tuning for translation tasks. Programming & ML Skills • Strong programming skills in Python. • Experience with deep learning frameworks such as: PyTorch, TensorFlow, MXNet • Experience with MT/NLP toolkits such as: Fairseq, Marian, Sockeye, OpenNMT LLM & Advanced NLP Experience • Practical experience fine-tuning LLMs for translation, including: Qwen, DeepSeek, LLaMA • Expertise with LoRA / QLoRA and parameter-efficient fine-tuning methods. • Experience designing RAG pipelines for translation or multilingual NLP tasks. • Advanced knowledge of prompt engineering for translation workflows. Research & Publications • Published research in top NLP or MT conferences, such as: ACL, AMTA, EAMT, EACL, ACL, WMT or similar venues Systems & Deployment • Experience designing large-scale MT inference systems. • Experience building data pipelines and training corpora for NMT models. • Experience developing REST APIs for ML services. Soft Skills • Excellent communication and documentation skills. • Ability to collaborate with cross-functional teams and translate research into production systems.