Applied Scientist: Data Quality & Evaluation

Maxinsights; Santa Clara (CA); On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

Health insurance

Vision care

Dental coverage

401(k)

Paid holidays

PTO (Paid Time Off)

Sick leave

Job summary

Maxinsights in Santa Clara, CA is seeking an Applied Scientist/Research Engineer to improve training data quality and evaluation systems for AI models. You will design experiments, develop data curation and scoring methods, analyze model failures, and build tools to identify the data most valuable for training and improving AI systems, with emphasis on active learning and human-in-the-loop evaluation.

Collaborate with research, engineering and data operations teams to turn findings into scalable

Qualifications

• Experience in applied ML research, data science, AI evaluation or related field.
• Strong Python skills and experience with ML frameworks and data processing tools.
• Experience with data curation, dataset optimization, model evaluation or active learning.

Responsibilities

• Develop training data curation, quality scoring, and data mixing methods.
• Design evaluation frameworks and benchmarks to measure model capabilities.
• Analyze model failures to connect data characteristics with performance.
• Apply active learning and human-in-the-loop evaluation to improve data efficiency.
• Build automated tools and pipelines to improve data quality and reduce manual effort.
• Run experiments to measure data improvements on model performance.
• Collaborate with research, engineering, and data operations to scale solutions.

Skills

Python

Data quality

Experiment design

Model evaluation

Communication

Job description

Maxinsights in Santa Clara, CA is seeking an Applied Scientist/Research Engineer to improve training data quality and evaluation systems for AI models. You will design experiments, develop data curation and scoring methods, analyze model failures, and build tools to identify the data most valuable for training and improving AI systems, with emphasis on active learning and human-in-the-loop evaluation.

Collaborate with research, engineering and data operations teams to turn findings into scalable