This role pays more than
61%
of similar roles. Most pay
$197,925–$254,750
— the shaded band above.
At the midpoint, this role pays about
$242k
versus about
$226k
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.
The LLM Machine Learning Engineer, Models and Agent Science, AIML joins the Intelligence Agents, Infrastructure, and Research team to develop groundbreaking machine learning capabilities and drive innovations in large language modeling. This role involves evaluating emerging research for real-world viability, translating cutting-edge ideas into product contexts, and bridging the gap between research and shipping at scale. Key responsibilities include post-training model optimization, developing interpretability or stress-testing algorithms, steering model behavior, and building agent harnesses. The candidate will utilize Python, UNIX skills, and agentic coding tools to conduct scientific investigations in neural network optimization and model training. The work focuses on the technical challenges of optimizing, interpreting, and developing new algorithms for both on-device and server-based Apple Foundation Models while addressing the constraints of practical product deployment within a collaborative research environment.
Develop and optimize large language models (LLMs) for both on-device and server-based environments.
Evaluate and reproduce recent research to determine its practical viability for Apple products.
Implement post-training techniques including model steering, interpretability, and performance optimization.
Build agent harnesses and develop algorithms to steer model behavior.
Conduct scientific investigations and experiments to overcome technical hurdles in a research environment.
Design rigorous experiments and ablation studies to benchmark LLM optimizations.
Translate cutting-edge machine learning research into scalable features for real-world products.
Develop software using Python and UNIX environments while utilizing agentic coding tools.
What we're looking for
A BS and 5+ years of experience, MS and 3+ years of experience, or PhD and 1+ year of experience is required.
Experience in post-training large language models (LLMs), including quality/performance optimization, interpretability, steering, or building agent harnesses.
Strong software engineering background with proficiency in Python and UNIX environments.
Ability to use agentic coding tools and conduct research in neural network optimization or model training.
Proven ability to evaluate recent research and translate it into viable products under real-world constraints.
Experience working with large-scale compute infrastructure and experimental rigor in benchmarking LLM optimizations.
A publication record at top AI/ML venues is preferred.