Staff Senior Machine Learning Engineer, Search & Knowledge Platforms

Apple Inc

Confirmed live yesterday Trusted

Quick summary

Work type: On-site
Location: Seattle, WA
Salary: $175,000–$308,500 / yr
Posted: 17 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Competitive pay

How this pay compares to similar roles

Similar $234k

This role $242k

$159k most similar roles pay here $325k

This role pays more than 53% of similar roles. Most pay $210,556–$256,950 — the shaded band above. At the midpoint, this role pays about $242k versus about $234k 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 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 · Staff Senior Machine Learning Engineer, Search & Knowledge Platforms

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Staff/Senior Machine Learning Engineer, Search & Knowledge Platforms joins the AI, Search & Knowledge Platforms team to improve the quality and performance of the search and ranking stack powering world knowledge question answering. This role involves developing sophisticated machine learning systems, robust software heuristics, and large language models to understand user queries, retrieve relevant documents, and provide direct answers. The position requires a versatile skill set including data science, opportunity analysis, ML system design, pipeline development, and quality evaluation. Candidates must be proficient in Python, Go, or similar languages while demonstrating expertise in information retrieval, search ranking, natural language processing, and prompt engineering. Key technical tasks include building scalable data pipelines, performing A/B testing, and optimizing inference performance for large models within complex architectures to solve core problems in search systems and knowledge-based query fulfillment.

What does a Machine Learning Engineer earn in Washington?

Median $241750 from 66 postings across 14 companies.

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Skills

Machine Learning Python Go Large Language Models (LLMs) Natural Language Processing (NLP) Information Retrieval Retrieval-Augmented Generation (RAG) Prompt Engineering Data Science A/B Testing Statistical Hypothesis Testing Data Pipelines Distributed Inference Model Quantization Search Ranking

What you'll do

Improve the quality and performance of search and ranking systems for world knowledge question answering.
Develop sophisticated machine learning models, software heuristics, and prompt engineering solutions to satisfy user intent.
Design reliable and scalable methods to measure and evaluate the impact of different technical solutions on system performance.
Build and maintain high-scale data pipelines and backend infrastructure for search and ranking systems.
Translate ambiguous product requirements into concrete engineering tasks and modeling goals.
Navigate and contribute to complex, large-scale codebases and architectures.
Perform rigorous data analysis to communicate findings and recommendations to cross-functional teams and leadership.

What we're looking for

Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related field (preferred).
6+ years of experience shipping Search and ML systems.
4+ years of experience shipping Search and ML systems (preferred).
Strong programming skills in Python, Go, or similar languages with a foundation in software design, data structures, and algorithms.
Experience building scalable data pipelines, backend systems, and navigating complex codebases.
Proficiency in data science methodologies including A/B testing, statistical hypothesis testing, and defining evaluation metrics.
Experience with prompt engineering, Information Retrieval, Search Ranking, NLP, RAG architectures, and LLM optimization (preferred).

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