This role pays less than
73%
of similar roles. Most pay
$201,250–$254,750
— the shaded band above.
At the midpoint, this role pays about
$203k
versus about
$228k
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
2305 open roles
on FindRole.
Listed pay typically runs
$175,000–$280,000
across 1873 roles with salary data.
AIML - Machine Learning Research is a role within the Applied ML team focused on enhancing features like Siri, Spotlight, and Safari through advanced intelligence. You will conduct large-scale machine learning and deep learning research to improve Open Domain Question Answering and Summarization by utilizing structured knowledge graph data and unstructured web data. Your daily work involves developing sophisticated large language models to understand user queries, retrieve relevant documents, and synthesize information for direct answers. Key responsibilities include designing advanced Reinforcement Learning technologies, including SFT, RLHF, RLAIF, Reward Modeling, Chain-of-thought, and agentic LLM systems. You will manage the end-to-end machine learning lifecycle, from algorithm design and data collection to model training and deployment. The role requires expertise in natural language processing, conversational AI, and translating cutting-edge generative AI research into production-ready technologies for personal data summarization.
Develop and improve Open Domain Question Answering systems using structured knowledge graphs and unstructured web data.
Design and develop advanced Reinforcement Learning technologies for the post-training of generative models.
Build and fine-tune domain-specific Large Language Models for tasks like summarization and information retrieval.
Create novel RL algorithms and integrate cutting-edge research into production-ready systems.
Manage the end-to-end machine learning lifecycle including data collection, training, evaluation, and deployment.
Develop LLM features to summarize personal data such as emails, messages, and notifications.
Translate theoretical advancements in generative AI into deployable technologies for Apple products.
What we're looking for
MS or Ph.D. in Computer Science, Machine Learning with a specialty in reinforcement learning, or a related field.
1+ years of ML experience in search, natural language processing/understanding, and conversational AI.
Proven experience in LLM post-training including SFT, RLHF, RLAIF, Reward Modeling, Chain-of-thought, and agentic LLM.
Hands-on experience building RL pipelines and training agents in simulation or real-world environments.
Experience as a researcher in machine learning areas such as natural language or speech.
Deep expertise in reinforcement learning-based post-training on LLM models, reward modeling, RLHF, RLAIF, Chain-of-thought, and agentic AI R&D (preferred).
Experienced researcher with publications in areas of machine learning, including natural language processing (preferred).
Deep understanding of cutting edge RL algorithms, large language model pre-training, and post-training (preferred).
Machine Learning
Deep Learning
Natural Language Processing
Large Language Models
Generative AI
Python
Go
Java
C++
Spark
Hadoop MapReduce
Hive
Impala
Information Retrieval
Data Mining
Prompt Engineering
Word Embeddings
Distributed Data Processing
Machine Learning
Deep Learning
Natural Language Processing
Large Language Models
Generative AI
Python
Go
Java
C++
Spark
Hadoop MapReduce
Hive
Impala
Information Retrieval
Prompt Engineering
Data Mining
Word Embeddings
Python
PyTorch
JAX
TensorFlow
Large Language Models
Deep Learning
Reinforcement Learning
Natural Language Processing
Speech Recognition
Reward Modeling
On-policy Distillation
Foundation Models
Python
PyTorch
JAX
TensorFlow
Large Language Models
Deep Learning
Reinforcement Learning
Natural Language Processing
Speech Recognition
Reward Modeling
On-policy Distillation
Foundation Models
Python
PyTorch
JAX
TensorFlow
Large Language Models
Deep Learning
Reinforcement Learning
Natural Language Processing
Speech Recognition
Reward Modeling
On-policy Distillation
Foundation Models