This role pays less than
70%
of similar roles. Most pay
$194,262–$254,750
— the shaded band above.
At the midpoint, this role pays about
$203k
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
1957 open roles
on FindRole.
Listed pay typically runs
$175,000–$277,600
across 1579 roles with salary data.
Machine Learning Research Engineer, ASE Search joins the Apple Services Engineering team to develop next-generation search and conversational discovery features across various platforms including iOS, macOS, and tvOS. The role involves building and improving machine learning models for retrieval, ranking, query understanding, and document understanding. You will manage the end-to-end model lifecycle, from data preparation and training through production deployment using Go, Java, Python, and Scala. Key technical requirements include expertise in generative AI systems, Large Language Models, Transformers, RAG, and fine-tuning. Candidates should be proficient with PyTorch or TensorFlow and familiar with distributed training tools like Ray, TensorRT, or vLLM. The work focuses on solving complex information retrieval problems to help users discover media content across the Apple ecosystem through advanced search, recommendation systems, and conversational engines.
Build and improve ML models for retrieval, ranking, query understanding, and document understanding.
Develop end-to-end ML pipelines including data preparation, training, evaluation, and production deployment.
Implement backend components using languages such as Go, Java, Python, or Scala.
Apply research in search, information retrieval, and generative AI to improve existing systems.
Design and run A/B experiments to measure and maintain the health of production systems.
Develop next-generation search and conversational discovery features for Apple devices and platforms.
Create automated testing, monitoring, and alerting systems for production environments.
What we're looking for
MS in Computer Science or a related subject area.
2+ years of relevant industry experience in ML or data systems.
Knowledge of generative AI systems, including Large Language Models, Transformers, RAG, and fine-tuning.
Experience with one or more ML frameworks such as PyTorch or TensorFlow.
Familiarity with search, recommendation systems, conversational engines, or related domains.
Strong communication skills and the ability to work effectively in a collaborative team environment.
Ph.D. in Computer Science or a related subject area (preferred).
4+ years of relevant industry experience in ML or data systems (preferred).
Experience building search or conversational capabilities such as query understanding, retrieval, ranking, indexing, autocomplete, or intent resolution (preferred).
Familiarity with big data pipelines using Scala, Python, or Apache Spark (preferred).
Exposure to distributed backend services, Kubernetes, cloud infrastructure, or container orchestration (preferred).
Experience with GoLang or gRPC services (preferred).
Familiarity with A/B experimentation and data-driven product development (preferred).
Machine Learning
Generative AI
Large Language Models
Transformers
RAG
PyTorch
TensorFlow
Python
Go
Scala
Apache Spark
Kubernetes
gRPC
TensorRT
vLLM
Ray
Information Retrieval
Generative AI
Large Language Models
Transformers
RAG
PyTorch
TensorFlow
Python
Go
Scala
Apache Spark
Kubernetes
gRPC
Information Retrieval
Ranking
Query Understanding
Data Pipelines
Machine Learning
Generative AI
Large Language Models
Transformers
Reinforcement Learning
RAG
PyTorch
TensorFlow
Ray
JAX
TensorRT
vLLM
Python
Go
Scala
Apache Spark
Kubernetes
LLM
PyTorch
TensorFlow
JAX
RAG
Natural Language Processing
Information Retrieval
Distributed Systems
Inference Optimization
Machine Learning
Deep Learning
LLM
Machine Learning
PyTorch
TensorFlow
JAX
Natural Language Processing
RAG
Information Retrieval
Distributed Systems
Inference Optimization
Deep Learning
Software Engineering