Machine Translation Researcher (NMT & LLMs)

Job Title: Machine Translation Researcher (NMT & LLMs)Degrees in Computational Linguistics or CS degrees.

Location: Fully Remote – India

Reports To: VP of AI

Employment Type: Full-time

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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.

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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.

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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.

Degrees in Computational Linguistics or CS degrees.

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.