Senior Machine Learning Engineer, Ads Predictions

Apple Inc

Confirmed live yesterday Trusted

Quick summary

Work type: On-site
Location: New York, NY
Salary: $216,200–$324,800 / yr
Posted: 148 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 $225k

This role $270k

$166k most similar roles pay here $342k

This role pays more than 87% of similar roles. Most pay $195,150–$254,750 — the shaded band above. At the midpoint, this role pays about $270k versus about $225k 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 · Senior Machine Learning Engineer, Ads Predictions

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Senior Machine Learning Engineer – Ads Predictions will join the Predictions group to build core machine learning models powering ad predictions and monetization across App Store and News platforms. The role involves designing and implementing models to improve user interaction, click-through rate, and conversion rate while developing retrieval algorithms using classical information retrieval and modern deep learning. You will work with large-scale distributed datasets to identify new signals and optimize marketplace outcomes. Key technical requirements include expertise in neural network architectures like Transformers, DNNs, and RNNs; experience with TensorFlow or PyTorch; and knowledge of reinforcement learning, contextual bandits, and LLM-based ranking signals. The candidate must be proficient in Python and familiar with SQL, Scala, or Java to manage high-volume data pipelines and production environments while solving complex problems in ad tech and recommender systems.

What does a Machine Learning Engineer earn in New York?

Median $216000 from 43 postings across 13 companies.

See salary data

Skills

Machine Learning Deep Learning Large Language Models Reinforcement Learning Information Retrieval Python TensorFlow PyTorch SQL Scala Java Neural Networks Transformers A/B Testing Multi-task Learning Contextual Bandits Data Pipelines

What you'll do

Design and implement machine learning models to improve predictions of user interaction, click-through rates, and conversion rates.
Develop and optimize retrieval algorithms using both classical information retrieval techniques and modern deep learning.
Implement advanced modeling techniques including deep neural networks, contextual bandits, multi-task learning, and LLM-based ranking signals.
Analyze large-scale distributed datasets to identify new signals and improve model accuracy and robustness.
Design and execute large-scale experiments to validate new model architectures and learning strategies.
Operationalize emerging technologies in Large Language Models, Reinforcement Learning, and representation learning for ad prediction systems.

What we're looking for

Bachelor's degree or equivalent experience in Computer Science, Machine Learning, AI, Information Retrieval, or a related field.
Master's or PhD degree in Computer Science, Machine Learning, AI, Information Retrieval, or a related field is preferred.
6+ years of experience applying machine learning and statistical modeling at scale, preferably in ad tech, recommender systems, or web-scale search/retrieval.
Deep experience with neural network architectures like Transformers, DNNs, and RNNs using TensorFlow or PyTorch.
Practical understanding of reinforcement learning, explore/exploit strategies, and bandit-based optimization.
Experience working with high-volume data pipelines, A/B testing infrastructure, and performance measurement at scale.
Proficiency in Python and familiarity with SQL, Scala, or Java for production environments.
Strong foundation in information retrieval, including query-document matching, embedding-based ranking, and learning-to-rank algorithms.