Machine Learning Engineer - Natural Language Generation, Input Experience

Apple Inc

Quick summary

Work type
On-site
Location
Seattle, WA
Salary
$139,500–$210,100 / yr
Posted
41 days ago

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $221k
This role $175k
$125k most similar roles pay here $277k

This role pays less than 80% of similar roles. Most pay $192,525–$249,750 — the shaded band above. At the midpoint, this role pays about $175k versus about $221k for comparable roles.

Based on 239 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 638 open roles on FindRole.

Listed pay typically runs $171,600–$272,100 across 505 roles with salary data.

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At a glance

TL;DR · Machine Learning Engineer - Natural Language Generation, Input Experience

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As a Machine Learning Engineer at Apple's Input Experience NLP team, you will play a pivotal role in advancing the next generation of AI applications by building scalable data and model pipelines that enhance user experience across various platforms. Your responsibilities include developing robust toolkits for efficient data synthesis and prompt engineering, as well as defining automated evaluation mechanisms to continuously improve model quality based on user feedback. You will collaborate closely with cross-functional teams to integrate cutting-edge ML techniques into features like Summarization and Smart Reply, ensuring privacy-preserving solutions that cater to diverse linguistic needs globally. This role requires expertise in Python programming, machine learning, and natural language processing, along with familiarity with large language models and MLOps practices.

Skills

Python CI/CD MLOps LLMs RHLF prompt engineering data synthesis automatic evaluation RAG

What you'll do

  • Develop and maintain data and model pipelines for production deployment.
  • Build toolkits to iterate on model quality through data synthesis and prompt engineering.
  • Define robust automated evaluation mechanisms to enhance model performance.
  • Analyze user feedback failures to identify model shortcomings and improve evaluations.
  • Research state-of-the-art techniques to boost model quality and robustness.
  • Implement experiments and simulations to assess the impact of model changes.

What we're looking for

  • MS or PhD in Computer Science or related field
  • Strong Python programming skills with production-quality module development experience
  • Experience building and maintaining end-to-end model pipelines from data curation to evaluation
  • Solid background in machine learning, natural language processing, and statistics
  • Familiarity with LLMs including SFT, RHLF, prompt engineering, and automatic evaluation techniques

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