Machine Learning Scientist, GenAI & ML Frameworks

Apple Inc

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
San Francisco, CA
Salary
$184,700–$324,800 / yr
Posted
80 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $224k
This role $255k
$157k most similar roles pay here $343k

This role pays more than 84% of similar roles. Most pay $192,987–$254,750 — the shaded band above. At the midpoint, this role pays about $255k versus about $224k 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 Scientist, GenAI & ML Frameworks

Machine Learning Scientist - Apple Services Engineering, GenAI & ML Frameworks joins the GenAI & ML Frameworks team to bridge foundation model capabilities with real-world production systems. This role focuses on improving LLM domain knowledge, tool use, reasoning, and system integration across various product areas. The successful candidate will manage end-to-end development including LLM training through continual pretraining, post-training, and preference optimization using GRPO-style methods. Responsibilities include developing agentic systems with multi-turn reliability and performing deployment-aware optimizations for latency, cost, and reliability. The role requires expertise in Python, deep learning toolkits like Jax, TensorFlow, or PyTorch, and a strong grasp of Natural Language Processing. You will build robust tooling for synthetic data generation and training pipelines while solving complex problems related to integrating cutting-edge models into user-facing features at scale.

What you'll do

  • Perform LLM continual pretraining and post-training, including preference optimization and RL methods like GRPO.
  • Develop agentic systems focusing on tool schemas, multi-turn reliability, and verifier-based learning loops.
  • Optimize models for production by balancing latency, cost, and reliability trade-offs.
  • Build robust tooling for synthetic data generation, evaluation harnesses, and training pipelines.
  • Translate LLM research into shipped features across various product lines at scale.
  • Lead cross-functional initiatives from initial problem definition through execution and scaling.
  • Improve LLM domain knowledge, tool use, reasoning, and system integration.

What we're looking for

  • BS/MS or PhD in a quantitative field such as Computer Science, Mathematics, Statistics, or Physics.
  • Proficient programming skills in Python.
  • Hands-on experience with deep learning toolkits including Jax, TensorFlow, or PyTorch.
  • Proven track record in training large models or building large-scale distributed systems.
  • Deep understanding of Deep Learning and Large Language Models (LLMs).
  • Expertise in Natural Language Processing.
  • Experience in LLM training techniques like continual pretraining, post-training, and preference optimization.
  • Experience with agentic systems, including tool schemas, multi-turn reliability, and synthetic data generation.

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