This role pays less than
60%
of similar roles. Most pay
$177,250–$254,750
— the shaded band above.
At the midpoint, this role pays about
$203k
versus about
$216k
for comparable roles.
Based on 240 similar postings.
Employer
About Apple Inc
Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
Apple Inc currently has
1984 open roles
on FindRole.
Listed pay typically runs
$175,000–$277,600
across 1590 roles with salary data.
Sr. Applied Scientist, AI Evaluation & Quality Systems joins the Human-centered AI, ML Data Quality Operations team to build scalable systems and methodologies that make AI evaluation trustworthy. The role involves developing novel quality control solutions, designing ground truth generation pipelines across various task types and modalities, and building real-time monitoring systems to detect drift or distribution shifts in live data. You will create calibration frameworks for LLM evaluators, develop root-cause analysis tools to identify disagreement patterns between automated and human judgments, and partner with downstream teams to refine guidelines. The position requires expertise in generative AI evaluation methodologies, including LLM-as-a-Judge design and failure mode analysis. Candidates must possess strong software engineering fundamentals, proficiency in Python and relevant machine learning frameworks, and experience building production-grade pipelines for large language models and agents within the domain of data quality.
What does a Applied Scientist earn?
Median $208800 from 47 postings across 10 companies.
Design and implement scalable ground truth generation pipelines across various task types and annotation modalities.
Build real-time monitoring systems to detect drift, distribution shifts, and quality degradation in live pipelines.
Develop calibration frameworks to re-anchor LLM evaluators against human-verified gold sets.
Create root-cause analysis tools to identify disagreement patterns between automated and human judgments.
Translate findings from evaluation data into actionable improvements for annotator training and guideline refinement.
Prototype, validate, and ship production-grade software for evaluating generative AI systems.
Collaborate with ML teams and developers to ground system designs in practical user feedback.
What we're looking for
5+ years of industry experience in applied science or machine learning with production-grade evaluation, annotation, or quality-assurance pipelines.
Experience designing ground truth generation pipelines across varied task types and annotation modalities, including cold-start scenarios.
Experience building real-time monitoring or anomaly/drift detection systems for live data or ML pipelines.
Working knowledge of generative AI evaluation methodology, including LLM-as-a-judge design, meta-evaluation, failure mode analysis, and calibration techniques.
Proficiency in Python and relevant ML frameworks with experience deploying and monitoring LLM-based pipelines and agents.
Ability to communicate findings clearly to both technical and non-technical stakeholders while incorporating feedback from downstream users.
MS or PhD in Computer Science, Machine Learning, Statistics, or a related quantitative field, or equivalent practical experience.
Experience designing systems that are configurable and extensible by other practitioners (preferred).