remote
AI/ML Research Engineer, LLM Post Training & Evaluation - Innodata
Research Engineer
Lead post‑training research and evaluation for large language models, driving model robustness, bias mitigation, and performance metrics using Python and advanced ML techniques.
About the role
Key Responsibilities
- Design and implement post‑training pipelines for large language models, including fine‑tuning, pruning, and quantization.
- Develop and maintain evaluation frameworks to assess model safety, bias, and factual accuracy across diverse datasets.
- Collaborate with data engineering teams to ingest, clean, and curate high‑quality training and evaluation corpora.
- Analyze model outputs, generate insights, and recommend architectural or training adjustments to improve performance.
- Document methodologies, results, and best practices for internal knowledge sharing and external reporting.
Requirements
- Strong background in Machine Learning with hands‑on experience in training and evaluating large language models.
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
- Experience with NLP evaluation metrics, bias detection, and safety assessment.
- Excellent analytical skills and ability to translate complex findings into actionable recommendations.
- Effective communication skills for cross‑functional collaboration and technical documentation.
Skills
pythonmachine learningnatural language processing