Staff Senior Machine Learning Engineer, Search & Knowledge Platforms

Apple Inc

Confirmed live yesterday Trusted

Quick summary

Work type: On-site
Location: Santa Clara, CA
Salary: $184,700–$324,800 / yr
Posted: 17 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $234k

This role $255k

$168k most similar roles pay here $342k

This role pays more than 72% of similar roles. Most pay $210,556–$256,950 — the shaded band above. At the midpoint, this role pays about $255k versus about $234k 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 · 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 successful candidate will perform data science, opportunity analysis, ML system design, pipeline development, and quality evaluation while navigating complex codebases. Required skills include proficiency in Python or Go, experience with prompt engineering, A/B testing, and statistical hypothesis testing. Technical expertise includes information retrieval, search ranking, natural language processing, and Retrieval-Augmented Generation architectures. The role focuses on optimizing inference performance, latency, and resource efficiency for large machine learning systems to solve complex problems in the search and knowledge domain.

What does a Machine Learning Engineer earn in California?

Median $246394 from 172 postings across 28 companies.

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Skills

Machine Learning Python Go LLMs Prompt Engineering Information Retrieval Natural Language Processing RAG Data Pipelines A/B Testing Statistical Hypothesis Testing Search Ranking Model Quantization Distributed Inference Data Science

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 large, complex, and unfamiliar codebases across the entire technology stack.
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, NLP, RAG architectures, and LLM optimization (preferred).

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