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AI Research Scientist

Job in San Jose, Santa Clara County, California, 95199, USA
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.
Job Description

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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