Machine Learning Systems Engineer, Siri Agent Modeling

Apple Inc

Confirmed live 2 days ago High trust

Quick summary

Work type
On-site
Location
Cupertino, CA
Salary
$150,400–$277,600 / yr
Posted
180 days ago
Freshness
Confirmed live 2 days ago

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Salary context

Competitive pay

How this pay compares to similar roles

Similar $225k
This role $214k
$135k most similar roles pay here $293k

This role pays less than 57% of similar roles. Most pay $195,000–$254,750 — the shaded band above. At the midpoint, this role pays about $214k versus about $225k 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.

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At a glance

TL;DR · Machine Learning Systems Engineer, Siri Agent Modeling

As a Machine Learning Systems Engineer, Siri Agent Modeling, you will join the Siri organization to optimize model training and inference across the machine learning stack. You will work with modeling teams and cross-functional partners in data, evaluation, and deployment to ensure models perform well and run efficiently on custom Apple Silicon. Your daily responsibilities involve writing production-level code to train and deploy models that provide personal context understanding and on-screen awareness for users. The role requires proficiency in Python, PyTorch, and compiled languages like Swift or C/C++. You will utilize techniques such as quantization, KV Cache, Speculative Decoding, and parallelism methods like FSDP and DDP. Experience with Transformer architectures, HuggingFace, and Megatron-LM is essential for solving the technical challenges of scaling large language models within a privacy-focused ecosystem.

What you'll do

  • Optimize model training and inference performance across the full machine learning stack.
  • Improve model execution speed and efficiency specifically on Apple Silicon hardware.
  • Write production-level code to train and deploy models for Siri's ecosystem.
  • Implement LLM optimization techniques such as quantization, KV Cache, and Speculative Decoding.
  • Manage the end-to-end model lifecycle including training, evaluation, and deployment.
  • Utilize parallel training methodologies like FSDP and DDP to scale model development.
  • Develop and deploy models that incorporate personal context and on-screen awareness.

What we're looking for

  • Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent experience.
  • Experience in the model lifecycle including training, evaluation, and deployment.
  • Strong understanding of Machine Learning architectures like Transformers and CNNs.
  • Proficiency in Python and ML frameworks such as PyTorch.
  • Experience with agentic AI-assisted coding.
  • Expertise in LLM optimization techniques like quantization, KV Cache, and Speculative Decoding.
  • Familiarity with training methodologies including FSDP, DDP, and other parallelism techniques.
  • Proficiency in a compiled programming language such as Swift, C/C++, or Java.

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