PHD in Biology

About Innodata:

Innodata (NASDAQ: INOD) is a leading data engineering company serving over 2,000 customers worldwide. We are the AI solutions provider-of-choice for four of the five largest global technology companies, as well as top-tier organizations in finance, insurance, law, healthcare, and more.

With a global workforce of over 5,000 employees and presence in 13 cities across the US, Canada, UK, Germany, Israel, India, Sri Lanka, and the Philippines, we combine advanced ML/AI technologies, subject matter expertise, and secure infrastructure to unlock the full potential of artificial intelligence.

About the Role:

We are seeking highly analytical and detail-oriented scientific experts to support our AI training and model evaluation initiatives. This role is ideal for PhDs in Physics, Chemistry, Biology, or Computer Science who have a passion for research, critical thinking, and applying domain-specific knowledge to cutting-edge AI applications.

You will contribute to the development and improvement of AI systems, including large language models (LLMs) and other machine learning pipelines, by creating, curating, and evaluating scientific datasets, validating model outputs, and providing domain-specific insights.

Key Responsibilities:

• Create, review, or annotate high-quality scientific content and datasets to train or evaluate AI systems.
• Perform quality assurance on model-generated outputs for scientific accuracy, clarity, and alignment with domain knowledge.
• Analyze and interpret AI behavior in the context of domain-specific tasks and error patterns.
• Support the development of guidelines for scientific content generation and annotation.
• Collaborate with internal engineering, data, and linguistic teams to ensure accuracy and consistency across projects.
• Conduct domain-specific research and synthesize findings to guide model improvements.
• Identify and resolve issues related to ambiguity, bias, or misrepresentation in scientific content.

Qualifications:

• PhD in Biology
• Strong analytical skills and ability to apply theoretical knowledge to real-world datasets and AI systems.
• Familiarity with scientific writing standards, peer-reviewed publishing, or lab-based research methodology.
• Attention to detail and ability to critically evaluate scientific content for accuracy and clarity.
• Excellent writing, editing, and communication skills.
• (Preferred) Experience with AI/ML concepts, data annotation, programming, or computational modeling.

Nice to Have:

• Experience working with large datasets or scientific databases.
• Knowledge of machine learning pipelines, NLP, or LLMs.
• Previous experience in interdisciplinary research or technical consulting.