Senior ML Researcher: Multimodal LLM Post-Training

Apple; San Diego (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Apple is seeking a Senior ML Researcher focused on foundation model post-training, including SFT and RL, to advance multimodal capabilities across products.

You will collaborate with researchers, engineers, and product teams to deliver real-world AI that enhances creative workflows and aligns with Apple's human-centered design.

Qualifications

• MS in Computer Science, Machine Learning, or related field, or equivalent practical experience.
• Minimum 4+ years of research experience in Multi-Modal LLM training and evaluation.
• Proven track record in multimodal SFT plus distillation/priority optimization/online RL with shipped models or rigorous experiments.
• Strong programming skills in Python and at least one deep learning framework such as JAX or PyTorch.

Responsibilities

• Formulate hypotheses for a use case.
• Define data collection and annotation.
• Build evaluations of model capabilities.
• Apply SFT, reward modeling, RL techniques, and distillation to improve the post-training recipe.
• Execute experiments, validate results, and communicate findings to the broader team.

Skills

Multimodal LLM research

Experiment design

Python programming

Data-driven intuition

Education

MS in CS/ML

PhD (preferred)

Tools

JAX

PyTorch

Job description

Summary

We are looking for a Senior ML Researcher who is passionate about foundation model post-training, including Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), and has experience with core capabilities such as instruction following, tool use, deep thinking, and reasoning. In this role, you will research, design, and deliver models where machine learning research meets real-world product impact. You will work closely with research scientists, engineers, and product teams to find new ways that generative AI can help our creative users. Your mission is to improve the workflows of millions of creators by combining generative AI with Apple's human-centered design principles.

Description

What makes our work unique is we have many research opportunities in real-world use cases in multimodal space. Our goal is to develop targeted, task-specific capabilities within our models across a broad range of areas, including video understanding, reasoning, instruction following, and agentic tasks.

The role involves contributing to every stage of the post-training process:

• Formulate hypotheses for a use case
• Define data collection and annotation
• Build evaluations of model capabilities
• Apply SFT, advanced reward modeling, RL techniques, and distillation to develop and improve the post-training recipe
• Execute experiments, validate results, and communicate findings to the broader team

If you're passionate about doing research that ships in Apple's legendary products, this is the team for you.

Minimum Qualifications

• MS in Computer Science, Machine Learning, or a related field, or equivalent practical experience
• 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, backed by shipped models or rigorous experiments
• Strong programming skills in Python and at least one deep learning, framework, such as JAX or PyTorch
• Ability to build intuition from a problem statement and translate it into dataset requirements, neural network design, and loss functions

Preferred Qualifications

• PhD in Computer Science, Machine Learning, or a related field, or equivalent practical experience
• Experience in GRPO, DPO, Vision-OPD, and visual-token compression
• Publications in top-tier ML conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV)
• Bonus: Experience in quantization, on-device models and SLM (Small Language Model)