AIML Researcher/Engineer - Foundation Model Post-Training at Apple

Cupertino, California, United States -

Full Time

Start Date

Immediate

Expiry Date

25 Aug, 26

Salary

0.0

Posted On

27 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Deep Learning, Large Language Models, Post-training, Reinforcement Learning, Python, JAX, PyTorch, Distributed Training, Complex Reasoning, Transformer Architectures, Instruction Following, Tool Use, Deep Reasoning, Architectural Adaption, Evaluation Methodologies, Cross-functional Communication

Industry

Computers and Electronics Manufacturing

Description

We are a tight-knit group of researchers and engineers responsible for building large scale frontier foundation models at Apple. We believe the most interesting breakthroughs in deep learning happen when we bridge the gap between raw model capability and user-centric utility. DESCRIPTION In this role, you will play a critical role shaping the future of our LLM efforts, specifically in transforming our models into highly capable, intelligent assistants that power billions of Apple products. You will tackle core training challenges in instruction following, tool use, deep reasoning, and architectural adaption — designing models that deliver magical, deeply integrated, and privacy-forward experiences across the Apple ecosystem. You will work alongside a fast-growing team of world-class experts to explore novel training strategies, architectural adaptations, and advanced evaluation methodologies. MINIMUM QUALIFICATIONS Demonstrated expertise in deep learning with a focus on LLMs, post-training, or reinforcement learning, backed by a strong record of academic or real-world accomplishments in these or closely related domains. Proficient programming skills in Python and a major deep learning framework such as JAX or PyTorch. Masters/PhD, or equivalent practical experience, in Computer Science, Machine Learning, or a related technical field. PREFERRED QUALIFICATIONS Experience training state-of-the-art large models at scale, with familiarity in distributed training challenges and trade-offs. Experience improving model performance on complex reasoning tasks (math, coding, logic). Experience with various transformers architectures and its transformations. Strong communication skills and a passion for working cross-functionally across Research and Product teams.

Responsibilities

The role involves transforming foundation models into intelligent assistants by tackling challenges in instruction following, tool use, and deep reasoning. You will design privacy-forward experiences and explore novel training strategies and architectural adaptations.