Software Engineer, Generative AI & ML

Imagine what you could do here. At Apple, new ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.

The Customer Systems - Gen AI Solutions team is responsible for building multi-turn, conversational, agentic LLM-based applications and frameworks to support Apple customers across numerous lines of business. You’ll be contributing hands on to a team that consists of engineers, data scientists & researchers to enhance a multi-modal, multi-agent platform with a key focus on incorporating the latest models and advancements to improve, latency, cost and customer experience. This is an incredible opportunity to contribute innovation & research to a well established generative AI platform within Apple. There is a huge amount of opportunity and growth within this space!

Contribute to model development and fine-tuning workflows for generative AI features.

Design and evaluate retrieval strategies for grounding large models in product-relevant data.

Prototype and benchmark multi-round agentic solutions for conversational knowledge-intensive tasks.
Partner with data and platform engineers to ensure scalable deployment and monitoring.

Stay current with advancements in large language modeling, vector search, RAG, and LLM Agents.

Participate in architecture discussions with engineers and partner teams.
Collaborate with product and business stakeholders to incorporate customer experience requirements into AI solutions.
Support localization and rollout of AI features across multiple markets.

Minimum Qualifications

• 5+ years of hands-on experience in ML, backend engineering, data engineering

• 1-2 years of hands-on experience in training, fine-tuning, or evaluating LLMs

• Foundational understanding of RAG architectures and vector-based retrieval systems

• Exposure to multi-agent orchestration frameworks in Rust and Python.

• Familiarity with modern deep learning frameworks such as PyTorch, TensorFlow, or JAX.

• Experience with data preprocessing, tokenization, and pipeline automation.

• Proficiency in machine learning libraries (transformers, datasets).

• Strong problem-solving and collaboration skills, with the ability to learn quickly and adapt to production-grade systems.

• Experience working with Multi-modal LLMs to enable Voice capabilities is a plus or prior experience with STT, TTS systems.

• Experience with deploying to cloud environments (AWS, GCP, on-remote hybrid) is required.