Research Scientist IV, Information Science

This is a fully remote position, open to applicants in United States.

📋 Description

• Oversee the design, execution, and assessment of AI/NLP systems for evaluating scientific feasibility; develop and test integrated pipelines that merge literature-based discovery, structured knowledge extraction, simulation, and code-based experimentation; conduct experiments and benchmarking on a large scale.

• Design and sustain high-quality research codebases using Python and related tools; create automated experimentation frameworks, integrate simulations, and establish evaluation infrastructure; guarantee reproducible artifacts, thorough logging, and documentation for extensive empirical studies.

• Plan and conduct systematic empirical analyses of model behavior and experimental results; examine large datasets, intermediate outputs, and logs to evaluate system performance, enhance methods, and uphold research integrity.

• Lead and co-author papers for prestigious AI/NLP and machine learning conferences and journals; prepare manuscripts for peer review; develop clear technical presentations and share findings at national and international conferences.

• Formulate research directions, experimental methodologies, and project timelines; assist in the preparation of technical reports and research documentation as required.

• Facilitate collaboration, manage relationships, and offer technical guidance to student researchers when appropriate.

• Foster a collegial, respectful, and collaborative research atmosphere through professional and constructive communication.

• Collaborate with partners both within and outside the University for joint experiments, data integration, and research dissemination.

⛳️ Requirements

• Outstanding programming skills, especially in Python, along with strong software engineering principles.

• Proven experience in designing and maintaining significant research codebases.

• In-depth knowledge of AI/NLP research methodologies, including experimental design, evaluation, and benchmarking.

• Strong hands-on experience in empirical research, with extensive involvement in generating and analyzing large datasets, intermediate representations, and experimental outcomes.

• Familiarity with Linux-based research environments, Git, containerization (e.g., Docker), and reproducible workflows.

• Capability to work independently in a research-focused, small-laboratory environment.

• Excellent written and verbal communication skills.

🏝️ Benefits

• Engaging work environment that fosters innovation and collaboration.

• Opportunities for professional development and growth within the field.

• Access to cutting-edge research tools and resources.

• Supportive colleagues and a dynamic team culture.