This role pays more than
87%
of similar roles. Most pay
$175,750–$246,987
— the shaded band above.
At the midpoint, this role pays about
$255k
versus about
$211k
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.
As a Senior Research Engineer, Training Data Infrastructure in Foundation Models, you will join a team focused on the full training lifecycle of frontier foundation models. You will work at the intersection of software engineering and machine learning research to transform theoretical observations into scalable systems for data acquisition, processing, and repository management. Your daily responsibilities include architecting high-throughput distributed ingestion systems for petabytes of multimodal data, optimizing data formats for efficient access, and building automated pipelines for filtering, deduplication, and decontamination. You will also develop tools for scaling experiments and data mixture optimization. The role requires expert proficiency in Python and C++, along with experience in distributed systems, performance engineering, and cloud infrastructure like GCP or AWS. You will solve complex problems regarding reward modeling, training efficiency, and high-quality data curation for large model training.
LLMs
Supervised Fine-Tuning (SFT)
Reinforcement Learning
Python
PyTorch
JAX
Deep Learning
Distributed Training
Reward Modeling
Synthetic Data Generation
Data Filtering
Curriculum Learning
Algorithm Research
Large Language Models
Deep Learning
Python
PyTorch
JAX
TensorFlow
Reinforcement Learning
Pallas
Triton
Kernel Optimization
Data Research
LLM) architecture
Mid-training
On-policy Distillation
LLMs
Reinforcement Learning
Python
PyTorch
JAX
Distributed Training
Deep Learning
Data Filtering
Curriculum Learning
Synthetic Data Generation
Model Steering
Preference Optimization
Evaluation Methodologies
LLM
Reinforcement Learning
Python
PyTorch
JAX
Distributed Training
Data Filtering
Curriculum Learning
Synthetic Data Generation
Model Steering
Preference Optimization
Deep Learning
Machine Learning