Senior ML Research Engineer: Personalization & Agentic AI

Apple, Inc.; Cupertino (CA); On-site; USD 216,000 - 394,000; Full time

Benefits offered by this job

Medical and dental coverage

Employee stock programs

Tuition reimbursement

Relocation assistance

Job summary

Apple is seeking a Staff Research Engineer for post-training for personalization in Cupertino. You will develop algorithms that tailor LLMs to individual users across multimodal contexts and agentic experiences, pushing the boundaries of personalization.

Collaborate with system engineers to prototype bleeding-edge techniques, optimize context memory, and scale models with data infrastructure. Strong emphasis on research impact and practical deployment.

Qualifications

• PhD in EE/CS or related field with ML/vision focus.
• Strong ML and Generative Modeling fundamentals.
• Hands-on with pre-training or post-training of LLMs.
• Familiarity with distributed training.
• Proficient in ML toolkits, e.g., PyTorch.
• Publications or OSS contributions demonstrating research impact.

Responsibilities

• Develop novel algorithms for LLM post-training to enable personalization.
• Advance personalized intelligence across multimodal reasoning and context.
• Collaborate with system engineers to prototype bleeding-edge innovations.
• Interface with modeling and data infrastructure teams.
• Translate research into scalable, high-quality experiences.

Skills

Strong ML fundamentals

Generative Modeling

LLM post-training

Reinforcement learning

Distributed training

PyTorch

Publications / OSS contributions

Education

PhD in Electrical Engineering / Computer Science

Tools

PyTorch

Job description

Apple is seeking a Staff Research Engineer for post-training for personalization in Cupertino. You will develop algorithms that tailor LLMs to individual users across multimodal contexts and agentic experiences, pushing the boundaries of personalization.

Collaborate with system engineers to prototype bleeding-edge techniques, optimize context memory, and scale models with data infrastructure. Strong emphasis on research impact and practical deployment.