Evaluation & Insights Machine Learning Engineer

Apple Inc

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Quick summary

Work type: On-site
Location: Cupertino, CA
Salary: $184,700–$324,800 / yr
Posted: 111 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $229k

This role $255k

$157k $343k

below market most similar roles pay here above market

This role pays more than 83% of similar roles. Most pay $202,800–$254,750 — the blue band above. At the midpoint, this role pays about $255k versus about $229k for comparable roles.

Based on 240 similar postings.

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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 3497 open roles on FindRole.

Listed pay typically runs $174,149–$277,600 across 2702 roles with salary data.

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At a glance

TL;DR · Evaluation & Insights Machine Learning Engineer

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The Evaluation & Insights Machine Learning Engineer joins the Human-Centered AI team for Apple Products to evaluate and improve AI systems by combining data science, model behavior analysis, and qualitative insights. You will architect comprehensive evaluation suites for LLMs and multimodal models, identifying edge cases in reasoning, factuality, and safety. Responsibilities include developing deterministic and LLM-assisted scoring frameworks, extracting actionable signals for model training, and building scalable distributed inference pipelines. You will utilize Python, PyTorch, JAX, Hugging Face, Ray, vLLM, MLflow, and Weights & Biases. The role focuses on solving technical problems related to hallucination detection, model alignment via RLHF/DPO, and Retrieval-Augmented Generation. You will translate qualitative failure modes into programmatic guardrails to ensure AI experiences are reliable and aligned with human expectations.

Skills

Python PyTorch JAX Hugging Face LLMs MLOps CI/CD RAG vLLM Ray MLflow Weights & Biases RLHF DPO Fine-Tuning Vector Databases Prompt Engineering Representation Learning Embedding-based Clustering SelfCheckGPT

What you'll do

Architect and execute comprehensive evaluation suites for LLMs and multimodal models to identify edge cases and safety issues.
Develop deterministic, heuristic, and LLM-assisted evaluation frameworks to quantify human-perceived quality metrics.
Translate qualitative failure modes into quantifiable loss patterns and actionable data-mixture adjustments for model training.
Partner with engineering teams to refine model behavior using evaluation telemetry for prompt engineering and RAG strategies.
Apply advanced ML techniques like embedding-based clustering to systematically map error taxonomies and latent failure manifolds.
Develop MLOps workflows to codify evaluation metrics and automate regression testing across model checkpoints.
Architect scalable, distributed inference and processing pipelines for high-throughput model evaluation and automated annotation.
Build automated evaluation pipelines utilizing LLMs to assess outputs at scale with high correlation to human annotations.

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.
Experience building internal tools or automated pipelines for ML workflows using platforms like MLflow or Weights & Biases.
Knowledge of human factors, HCI, or cognitive science methodologies as applied to AI system design (preferred).

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