Applied Research Scientist, LLM Evaluation & Post-Training

Scope of the Role:

Innodata is expanding its GenAI research capability to advance state-of-the-art evaluation and post-training methods for LLM and multimodal systems. As an Applied Research Scientist, LLM Evaluation & Post-Training, you will lead research and experimentation on how evaluation design, measurement strategies, and feedback signals influence model improvement.

This role is ideal for a technically rigorous researcher who is deeply fluent in modern LLM evaluation and post-training, and who can turn research insight into practical methods for customer solutions and internal platform innovation. You will work across human-in-the-loop and AI-augmented workflows, partnering with Language Data Scientists and AI/ML Research Engineers to design and validate evaluation frameworks that drive measurable model gains.

The ideal candidate combines strong experimental and statistical judgment with hands-on technical ability and can engage as a peer with research and engineering stakeholders at leading AI companies.

What You’ll Own:

As an Applied Research Scientist, LLM Evaluation & Post-Training, you will help define the next generation of evaluation-driven model improvement workflows. You will study how different evaluation approaches (human, automated, hybrid) shape model selection and post-training outcomes, and you will design experiments that produce credible, actionable conclusions.

Your work may include designing benchmark datasets, developing evaluation taxonomies and protocols, defining metrics and scoring methodologies, analyzing failure modes, and testing how changes in evaluation setup affect downstream fine-tuning results. You will also support customer engagements by bringing scientific rigor to evaluation strategy, methodology review, and technical recommendations.

This is a highly collaborative role that sits at the intersection of research, engineering, and language/data operations. Additional responsibilities include (but are not limited to):

• Define and execute a research agenda focused on LLM evaluation and post-training, especially evaluation-driven model improvement
• Design rigorous experiments to study how evaluation methodologies impact fine-tuning and post-training outcomes
• Develop and validate evaluation frameworks for LLM and multimodal systems, including:
  • benchmark/task design
  • scoring methods
  • judge/model-assisted evaluation
  • human evaluation protocols
  • robustness/stress testing
• Lead research on advanced evaluation domains, including long-context, cross-modal, and dynamic multi-turn evaluations
• Study the effectiveness and limitations of existing evaluation techniques, and propose improved methodologies with clear validity and scalability tradeoffs
• Analyze model behavior and failure patterns; generate actionable recommendations for model improvement and evaluation redesign
• Collaborate with AI/ML Research Engineers to translate research methods into scalable evaluation and post-training pipelines
• Collaborate with Language Data Scientists to integrate human-in-the-loop and synthetic data/evaluation strategies into workflows