Machine Learning Engineer - Applied Science

Apple Inc

Quick summary

Work type: On-site
Location: Seattle, WA
Salary: $201,300–$302,200 / yr
Posted: 6 days ago
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $217k

This role $252k

$155k most similar roles pay here $318k

This role pays more than 80% of similar roles. Most pay $183,733–$249,750 — the shaded band above. At the midpoint, this role pays about $252k versus about $217k 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 1777 open roles on FindRole.

Listed pay typically runs $162,500–$272,100 across 1443 roles with salary data.

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At a glance

TL;DR · Machine Learning Engineer - Applied Science

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Join the Geo domain team as a Machine Learning Engineer where you will leverage innovative ML techniques and large language models to enhance query understanding, document ranking, and answer retrieval for Siri and Spotlight. You will lead the development of machine learning models that improve search quality across various stages including data collection, feature engineering, training, evaluation, and deployment in production environments. This role involves identifying high-impact research directions, translating theoretical findings into practical improvements, and collaborating with product and design teams to shape technical roadmaps. Ideal candidates have over 8 years of experience in information retrieval, NLP, machine learning, or deep learning, along with a strong grasp of ML theory and the ability to apply advanced techniques at scale. Experience with LLMs and generative AI is preferred for this role that demands both technical expertise and cross-functional collaboration skills.

Skills

Python TensorFlow PyTorch Keras Scikit-learn Spark MLOps Docker Kubernetes AWS Azure CI/CD SQL NoSQL PostgreSQL Redis Elasticsearch Prometheus Grafana Git Jupyter Swagger RESTful_APIs

What you'll do

Own the ML development cycle from analysis to deployment in production.
Lead development of models for improving search quality across retrieval and ranking.
Apply advanced techniques like learning-to-rank, embedding models, and reinforcement learning.
Independently identify high-impact research directions and drive them to production improvements.
Mentor junior engineers and provide technical leadership in ML system architecture.
Translate product requirements into technical designs and model architectures for user impact.

What we're looking for

8+ years experience in information retrieval, NLP, machine learning, or deep learning.
Deep understanding of ML theory including supervised learning, ranking models, embeddings, and evaluation metrics.
Proven ability to apply advanced ML techniques to improve search relevance at scale.
Comfortable leading experimentation, offline evaluation, and A/B testing for iterative improvements.
Active monitoring of recent research literature and translating findings into practical system improvements.

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