ML Safety Engineer

Apple Inc

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
San Francisco, CA
Salary
$184,700–$277,600 / yr
Posted
142 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $213k
This role $231k
$146k most similar roles pay here $292k

This role pays more than 65% of similar roles. Most pay $171,500–$254,750 — the shaded band above. At the midpoint, this role pays about $231k versus about $213k 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.

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At a glance

TL;DR · ML Safety Engineer

ML Safety Engineer As part of the Apple Services Engineering team, the ML Safety Engineer will lead the design and development of automated safety benchmarking methodologies for media-related agents. The role involves building rigorous evaluation frameworks, creating large-scale datasets for adversarial use cases across multiple languages, and defining metrics for complex multi-turn agentic interaction patterns. You will develop automated pipelines to judge model outputs against safety policies and translate experimental findings into actionable product improvements. Key technical requirements include proficiency in Python, including libraries like pandas, NumPy, PyTorch, and Jupyter, along with experience in LLMs, RAG systems, and reinforcement learning. The role requires expertise in constructing annotation guidelines, designing taxonomies, and applying statistical rigor to ensure AI models remain reliable and aligned with human expectations across various media and application marketplace use cases.

What you'll do

  • Design scientifically-grounded benchmarking methodologies for safety and responsibility across various media and application use cases.
  • Develop automated evaluation pipelines to judge and analyze model outputs against safety policies at scale.
  • Create and curate datasets representing realistic and adversarial scenarios across multiple languages and domains.
  • Define and validate new metrics for complex phenomena like multi-turn agentic interaction patterns.
  • Apply statistical rigor and reproducibility to all benchmarking and evaluation objectives.
  • Translate experimental findings into actionable model improvements and safety mitigations with engineering teams.
  • Monitor industry practices and academic research to ensure benchmarks remain relevant.

What we're looking for

  • Advanced degree (MS or PhD) in Computer Science, Software Engineering, or a related field like Data Science or Linguistics.
  • At least 1 year of work experience as a postdoc or in the industry.
  • Strong research background in empirical evaluation, experimental design, or benchmarking.
  • Proficiency in Python and associated libraries such as pandas, NumPy, Jupyter, and PyTorch.
  • Experience working with large datasets, annotation tools, and model evaluation pipelines.
  • Experience evaluating AI/ML models, specifically LLMs or program synthesis systems.
  • Familiarity with responsible AI safety, hallucination detection, and model alignment.
  • Ability to design taxonomies, categorization schemes, and structured labeling frameworks.

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