Staff Machine Learning Engineer, Ads Platform

Apple Inc

Confirmed live 2 days ago High trust

Quick summary

Work type: On-site
Location: Austin, TX
Posted: 9 days ago
Freshness: Confirmed live 2 days ago
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $232k

$170k most similar roles pay here $318k

This listing doesn't post a salary. Most similar roles pay $208,575–$255,487.

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 2202 open roles on FindRole.

Listed pay typically runs $175,000–$278,800 across 1802 roles with salary data.

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At a glance

TL;DR · Staff Machine Learning Engineer, Ads Platform

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Staff Machine Learning Engineer – Ads Platform joins the team to design and build machine learning systems and data pipelines focused on safeguarding advertiser trust and enhancing invalid traffic protections. This role involves managing the end-to-end lifecycle of models for high-throughput, low-latency production environments while executing an innovation roadmap that includes performance tuning, calibration, and drift detection. The engineer will develop distributed algorithms, implement scalable feature pipelines, and build tools for A/B testing and experimentation. Key technologies include Java, Python, or Scala for development, alongside big data frameworks like Spark, Kafka, Hadoop, or Flink. Candidates must navigate complex problems involving multilayered datasets to optimize existing libraries and infrastructure. The work specifically addresses the technical challenges of maintaining integrity within an advertising platform while ensuring high product quality and system performance across compute and storage layers.

What does a Machine Learning Engineer earn?

Median $233875 from 314 postings across 50 companies.

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Skills

Machine Learning Python Java Scala Spark Kafka Flink Hadoop CI/CD A/B Testing Distributed Systems Feature Stores Anomaly Detection Data Pipelines Agile

What you'll do

Manage the end-to-end lifecycle of machine learning models for high-throughput, low-latency production systems.
Design and optimize distributed algorithms and data processing frameworks using tools like Spark.
Build scalable feature pipelines to ingest, clean, and transform massive datasets.
Develop machine learning systems to safeguard advertiser trust and enhance invalid traffic protections.
Perform performance tuning, bottleneck analysis, and system optimization across compute and storage layers.
Build infrastructure for A/B testing, statistical evaluation, and experimentation pipelines.
Implement robust CI/CD workflows, feature stores, and streaming infrastructure like Kafka or Flink.

What we're looking for

BS or MS in Computer Science, Software Engineering, or related technical fields.
8+ years of experience building machine learning capabilities across many different product areas at scale.
Strong proficiency in Java, Python, or Scala for algorithm and system development.
Experience with distributed systems and big data frameworks such as Spark, Kafka, Hadoop, or Flink.
Solid understanding of data structures, algorithms, and system design principles.
Familiarity with CI/CD workflows, cloud environments, and containerized deployments.
Understanding of statistical methods, A/B testing, and online experimentation frameworks.
Experience with anomaly detection (preferred); background in advertising systems (preferred); contributions to open-source tools (preferred).