Senior ML Researcher: Multimodal LLM Post-Training

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)