Senior ML Research Engineer, Post-Training for Personalization

Apple Inc

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Quick summary

Work type
On-site
Location
Seattle, WA
Salary
$205,400–$374,300 / yr
Posted
11 days ago
Freshness
Confirmed live today
Nearby
99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $229k
This role $290k
$151k $398k
below market most similar roles pay here above market

This role pays more than 95% of similar roles. Most pay $202,800–$254,750 — the blue band above. At the midpoint, this role pays about $290k versus about $229k 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 3497 open roles on FindRole.

Listed pay typically runs $174,149–$277,600 across 2702 roles with salary data.

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At a glance

TL;DR · Senior ML Research Engineer, Post-Training for Personalization

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The Senior ML Research Engineer, Post-Training for Personalization joins the Intelligent System Experience team to develop core modeling technologies for Apple Intelligence. This role focuses on driving research in post-training methodologies to enable models to understand individual user needs, adapt to personal context, and deliver personalized experiences. You will work at the intersection of large language models, multimodal reasoning, and agentic systems, translating emerging research into scalable experiences. Key responsibilities include algorithm development for prompt optimization and post-training, utilizing techniques such as reinforcement learning, multimodal reasoning, and context compaction or memory systems. You will use PyTorch and interface with large-scale modeling and data infrastructure. The work involves solving technical problems related to personalization, agentic search, generative memories, and visual generation within generative models.

Skills

Large Language Models Reinforcement Learning PyTorch Multimodal Reasoning Distributed Training Post-Training Prompt Optimization Generative Modeling Agentic Systems Context Compaction Memory Systems Computer Vision Machine Learning Data Infrastructure

What you'll do

  • Develop novel algorithms for LLM post-training to advance personalized intelligence.
  • Drive research in post-training methodologies to help models adapt to personal user context.
  • Implement prompt optimization and reinforcement learning techniques for model training.
  • Translate emerging research into scalable, high-quality multimodal reasoning and agentic experiences.
  • Prototype and incorporate bleeding-edge algorithmic innovations into agentic systems.
  • Interface with large-scale modeling and data infrastructure to deploy generative models.
  • Build and deploy multimodal LLMs with tool-use and memory capabilities.

What we're looking for

  • PhD in Electrical Engineering/Computer Science or a related field, or comparable professional experience.
  • Strong fundamentals in machine learning and generative modeling.
  • Hands-on experience in pre-training or post-training of large language models and reinforcement learning.
  • Familiarity with distributed training.
  • Proficiency in using ML toolkits such as PyTorch.
  • Proven track record of research contributions through top-tier conference publications or open-source algorithm contributions.
  • Experience with building and deploying Multimodal-LLMs (preferred).
  • Experience building and deploying agentic systems and LLMs with tool-use and memory capabilities (preferred).

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