This role pays more than
78%
of similar roles. Most pay
$194,283–$254,750
— the shaded band above.
At the midpoint, this role pays about
$255k
versus about
$225k
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.
The Evaluation & Insights Machine Learning Engineer joins the Human-Centered AI team to evaluate and improve AI systems by combining data science, model behavior analysis, and qualitative insights. This role involves architecting evaluation suites for LLMs and multimodal models, developing advanced scoring frameworks like LLM-as-a-judge, and identifying edge cases in reasoning, factuality, and safety. The engineer will translate qualitative failure modes into quantifiable loss patterns to inform prompt engineering, RAG strategies, and model fine-tuning. Key responsibilities include building MLOps workflows, designing distributed inference pipelines using Ray or vLLM, and mapping error taxonomies through embedding-based clustering. Required skills include Python, PyTorch, JAX, Hugging Face, and experience with RLHF/DPO. The role addresses the challenge of ensuring AI experiences are reliable, safe, and aligned with human expectations across various product features.
Architect and execute comprehensive evaluation suites for LLMs and multimodal models to identify edge cases in reasoning, factuality, and safety.
Develop deterministic, heuristic, and LLM-assisted scoring frameworks to quantify human-perceived quality metrics like helpfulness and hallucination rates.
Translate qualitative failure modes into quantifiable loss patterns and actionable data-mixture adjustments for model training and inference.
Use advanced ML techniques like embedding-based clustering and perturbation analysis to map error taxonomies in model outputs.
Build automated evaluation pipelines using LLMs to assess outputs at scale with high correlation to human baselines.
Develop MLOps workflows to codify metrics, automate regression testing, and integrate assessments into CI/CD pipelines.
Architect scalable, distributed inference and processing pipelines for high-throughput model evaluation and automated annotation.
Partner with engineering teams to refine model behavior through prompt engineering, RAG strategies, and fine-tuning based on evaluation telemetry.
What we're looking for
Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Cognitive Science, or a related technical field.
8+ years of relevant industry experience in ML Engineering or Applied Research.
Advanced proficiency in Python and modern deep learning ecosystems including PyTorch, JAX, and Hugging Face.
Proven experience building scalable ML inference pipelines, model evaluation workflows, and structured rating frameworks for large-scale AI systems.
Hands-on experience developing, fine-tuning, or evaluating LLMs, multimodal models, and NLP systems.
Deep familiarity with AI quality metrics, hallucination detection, model alignment (RLHF/DPO), and LLM-as-a-judge frameworks.
Strong familiarity with advanced prompt engineering, RAG architectures, vector databases, and fine-tuning techniques.
Experience building internal tools or automated pipelines for ML workflows using platforms like MLflow or Weights & Biases.
Machine Learning
Computer Vision
Python
Data Analysis
Statistical Modeling
Deep Learning
Data Pipelines
Visualization
Synthetic Data
Data Augmentation
Model Monitoring
Generative AI
Root-Cause Analysis
Evaluation Frameworks