Research Engineer - NLP & Large Language Models

About the Role

Key Responsibilities

• Design, implement, and maintain training and post-training pipelines for large language and multimodal models (e.g., instruction tuning, alignment, preference optimization)
  
• Conduct research and engineering on post-training methods
  
• Contribute to multimodal modeling , integrating text with modalities such as vision, speech, or audio
  
• Explore and apply diffusion-based models and hybrid generative approaches for language and multimodal representation learning
  
• Optimize large-scale training and inference
• Develop evaluation pipelines and benchmarks for language understanding, reasoning, alignment, and multimodal performance
  
• Collaborate with researchers to prototype new ideas, reproduce results from the literature, and contribute to publications or technical reports
• Ensure code quality, reproducibility, and documentation suitable for long-term research and open-source release

Required Qualifications

• MSc or PhD in Computer Science, Machine Learning, AI, or a related field (or equivalent practical experience)
  
• Strong background in NLP and deep learning , with hands-on experience working with large language models
  
• Solid programming skills in Python , with experience using modern ML frameworks (e.g., PyTorch)
  
• Experience working with open-weight or open-data models , including releasing models, datasets, or benchmarks
• Familiarity with post-training techniques for LLMs (e.g., instruction tuning, preference optimization, alignment)
  
• Strong experimental rigor: ability to design controlled experiments, analyze results, and iterate efficiently
  

Desired / Bonus Qualifications

• Experience with diffusion models (e.g., text diffusion, latent diffusion, or multimodal diffusion)
  
• Hands-on work on multimodal models (e.g., text-image, text-audio, speech-language systems)
  
• Exposure to LLM alignment, safety, or evaluation beyond standard language modeling metrics
• Experience with distributed training and large-scale model experimentation
  
• Familiarity with multilingual or low-resource language settings
  
• Contributions to open-source ML or published research in NLP, multimodality, or generative modeling

What We Offer

• A research-driven environment with access to large-scale compute and modern ML infrastructure
  
• Close collaboration with leading researchers in NLP, multimodality, and generative modeling
  
• The opportunity to work on foundational, open, and socially responsible AI systems
  
• Support for publishing research, contributing to open-source projects, and engaging with the broader research community
  
• Competitive compensation and benefits, commensurate with experience

Information