Senior ML Researcher, Multimodal LLM Post-Training

Apple Inc

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Quick summary

Work type: On-site
Location: Cupertino, CA
Salary: $184,700–$324,800 / yr
Posted: 1 day 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 $255k

$161k $342k

below market most similar roles pay here above market

This role pays more than 79% of similar roles. Most pay $202,800–$254,750 — the blue band above. At the midpoint, this role pays about $255k versus about $229k for comparable roles.

Based on 240 similar postings.

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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 3452 open roles on FindRole.

Listed pay typically runs $166,600–$277,600 across 2651 roles with salary data.

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At a glance

TL;DR · Senior ML Researcher, Multimodal LLM Post-Training

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The Senior ML Researcher: Multimodal LLM Post-Training joins a team focused on foundation model post-training to improve workflows for creative users. You will research, design, and deliver models by formulating hypotheses, defining data collection and annotation, and building evaluations of model capabilities. Your daily work involves applying Supervised Fine-Tuning, advanced reward modeling, Reinforcement Learning, and distillation to develop post-training recipes. You will execute experiments and communicate findings to improve capabilities in video understanding, reasoning, instruction following, and agentic tasks. The role requires proficiency in Python and deep learning frameworks like JAX or PyTorch. You will utilize techniques such as GRPO, DPO, Vision-OPD, and visual-token compression to solve technical problems in the multimodal space and develop targeted, task-specific capabilities for generative AI products.

Skills

Python PyTorch JAX Reinforcement Learning Supervised Fine-Tuning Distillation DPO GRPO Multimodal LLM Quantization Neural Network Design Reward Modeling Preference Optimization

What you'll do

Formulate research hypotheses for specific multimodal use cases.
Define requirements for data collection and annotation processes.
Build comprehensive evaluations to measure model capabilities.
Apply SFT, reward modeling, RL techniques, and distillation to post-training recipes.
Execute experiments and validate results to improve model performance.
Translate problem statements into dataset requirements, neural network designs, and loss functions.
Communicate research findings to the broader engineering and product teams.

What we're looking for

MS in Computer Science, Machine Learning, or a related field, or equivalent practical experience.
PhD in Computer Science, Machine Learning, or a related field, or equivalent practical experience (preferred).
Minimum 4+ years of research experience in Multi-Modal LLM training and evaluation.
Proven track record in multimodal SFT plus at least one of distillation, preference optimization, or online RL.
Strong programming skills in Python and at least one deep learning framework such as JAX or PyTorch.
Ability to translate problem statements into dataset requirements, neural network design, and loss functions.
Experience in GRPO, DPO, Vision-OPD, and visual-token compression (preferred).
Publications in top-tier ML conferences such as NeurIPS, ICML, ICLR, CVPR, ICCV, or ECCV (preferred).

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