AI Research Scientist
Job in
San Jose, Santa Clara County, California, 95199, USA
Listed on 2026-01-22
Listing for:
Insight Global
Full Time
position Listed on 2026-01-22
Job specializations:
-
IT/Tech
Data Scientist, AI Engineer, Machine Learning/ ML Engineer, Artificial Intelligence
Job Description & How to Apply Below
Qualifications
- 3–5 years of experience in AI/ML research roles, ideally in applied or product-focused environments.
- Demonstrated success in delivering research-driven solutions that have been deployed in production.
- Experience collaborating in cross-functional teams across research, engineering, and product.
- Publications in top-tier AI/ML conferences (e.g., NeurIPS, ICML, ACL, CVPR) are a plus.
- Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field is strongly preferred.
- Candidates with a master’s degree and exceptional research or industry experience will also be considered.
Insight Global is seeking a multiple experienced, driven AI Research Engineer to join an established health technology company to join the team in San Jose, CA. This is a full-time, permanent role with competitive salary, bonus, and comprehensive benefits.
Responsibilities- Design and Run ML Experiments:
Set up and analyze machine learning experiments, build strong baselines, and choose the right evaluation metrics for each task. - Stay Current with AI Research:
Continuously explore new research developments and adapt cutting-edge techniques to solve real-world business problems. - Establish Evaluation Standards:
Define robust evaluation methods, including offline metrics, user studies, and adversarial (red team) testing, to ensure reliable and meaningful results. - Data & Annotation Strategy:
Determine data and labeling needs, create clear annotation guidelines, and manage quality assurance for labeled datasets. - Cross-Functional Collaboration:
Work closely with domain experts, product managers, and engineers to refine problem definitions and align on technical and business constraints. - Build Reusable Research Tools:
Develop and maintain reusable assets like datasets, modular code libraries, evaluation tools, and thorough documentation. - Optimize ML Pipelines:
Partner with ML Engineers to fine-tune training and inference workflows, ensuring smooth deployment into production environments. - Contribute to the Research Community:
Support academic publications and represent the company at conferences and in research forums when needed.
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