Senior ML Research Engineer — Personalization & Post-Training

Apple, Inc.; Seattle (WA); On-site; USD 205,000 - 374,000; Full time

Benefits offered by this job

Employee stock purchase plan

Discretionary bonuses or commission

Relocation assistance

Educational expense reimbursement

Medical and dental coverage

Retirement benefits

Job summary

Apple, Inc. in Seattle, WA is seeking a Staff Research Engineer focused on post-training for personalization of LLMs. You will advance algorithms at the intersection of large language models, multimodal reasoning, and agentic systems to deliver personalized user experiences.

The role emphasizes fluency in algorithm development (prompt optimization, post-training) and collaboration with world-class system engineers to prototype innovative solutions in a large-scale data environment.

Qualifications

• PhD or equivalent professional experience in ML/CV with emphasis on ML fundamentals.
• Strong ML and Generative Modeling fundamentals.
• Hands-on experience in post-training or pre-training of large language models and reinforcement learning.
• Familiarity with distributed training.
• Proficiency in using ML toolkits, e.g., PyTorch.
• Proven research contributions demonstrated through publications or open source work.

Responsibilities

• Research and advance post-training methodologies for personalized intelligence in LLMs.
• Drive algorithms that enable models to understand user context and preferences for personalization.
• Collaborate with system engineers to prototype bleeding-edge algorithmic innovations.
• Interface with large-scale modeling and data infrastructure.

Skills

ML fundamentals

Reinforcement learning

Post-training of LLMs

Distributed training

Education

PhD in Electrical Engineering/Computer Science or related field

Tools

PyTorch

Job description

Apple, Inc. in Seattle, WA is seeking a Staff Research Engineer focused on post-training for personalization of LLMs. You will advance algorithms at the intersection of large language models, multimodal reasoning, and agentic systems to deliver personalized user experiences.

The role emphasizes fluency in algorithm development (prompt optimization, post-training) and collaboration with world-class system engineers to prototype innovative solutions in a large-scale data environment.