This role pays less than
68%
of similar roles. Most pay
$192,050–$254,750
— the shaded band above.
At the midpoint, this role pays about
$203k
versus about
$223k
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.
Machine Learning Engineer, Human Centered AI - Evaluations & Insights joins the Human-Centered AI team for Apple Media Services. This role bridges the gap between human perception and algorithmic performance by evaluating and optimizing Foundation Models and generative AI systems. You will architect robust evaluation frameworks, design scalable MLOps pipelines for model assessment, and translate qualitative failure modes into programmatic guardrails and training signals like SFT and RLHF/DPO. Day-to-day responsibilities include building automated scoring frameworks, identifying edge cases in multi-step reasoning, and developing distributed inference pipelines using Ray and vLLM. You will utilize Python, PyTorch, JAX, Hugging Face, and tools like MLflow or Weights & Biases to improve model behavior through RAG strategies and fine-tuning. The role focuses on ensuring AI experiences are reliable and aligned with human expectations across search and recommendation features.
What does a Machine Learning Engineer earn in Washington?
Median $241750 from 66 postings across 14 companies.
Architect and execute comprehensive evaluation suites for LLMs and multimodal models to identify edge cases in reasoning, factuality, and safety.
Develop advanced scoring frameworks using deterministic methods and LLM-as-a-judge techniques to quantify human-perceived quality metrics.
Translate qualitative failure modes into quantifiable loss patterns, programmatic guardrails, and actionable data-mixture adjustments for model training.
Build automated MLOps workflows to codify evaluation metrics and integrate human-centric assessments into ML CI/CD pipelines.
Design scalable, distributed inference and processing pipelines for high-throughput model evaluation and automated annotation at scale.
Apply advanced machine learning techniques like embedding-based clustering to systematically map error taxonomies in model outputs.
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.
5+ 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 techniques, model alignment (RLHF/DPO), and LLM-as-a-judge frameworks.
Experience building internal tools or automated pipelines for ML workflows using platforms like MLflow or Weights & Biases.
Strong familiarity with advanced prompt engineering, RAG architectures, and Fine-Tuning; knowledge of human factors, HCI, or cognitive science (preferred).
Python
R
SQL
Generative AI
NLP
NLU
Data Pipelines
ETL
Human-in-the-loop
Machine Learning
Data Annotation
Statistical Process Control
AI Safety
Responsible AI
Data Quality Frameworks
Python
R
SQL
Generative AI
NLP
NLU
Data Pipelines
ETL
Human-in-the-loop
Machine Learning
Data Annotation
Statistical Process Control
AI Safety
Responsible AI
Data Quality Frameworks