Sequoia Software Engineer - Generative AI & ML, Customer Systems
Summary
Engineer on Apple's Customer Support AI team building multi-turn, multi-agent conversational applications for supporting Apple customers. Day-to-day: LLM development and fine-tuning, designing RAG/retrieval strategies, prototyping multi-agent reasoning systems, and scalable cloud deployment and monitoring. Core tech: ML libraries (transformers, datasets), LLMs, AWS/GCP.
At Apple, we are driven to deliver exceptional experiences through ultra-fast, thoughtfully designed, and meticulously crafted solutions. Our team is not just any group; we are a highly motivated, fast-paced, and dynamic collective of professionals committed to scaling new heights and achieving excellence. We seek individuals who strive beyond mediocrity and are relentless in their pursuit of perfection.
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-agent collaboration systems for structured reasoning tasks.
Partner with data and platform engineers to ensure scalable deployment and monitoring.
The Customer Support AI team is responsible for building multi-turn, conversational, agentic 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 research 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!
Minimum Qualifications
- 4+ years of hands-on experience in ML, backend engineering, data engineering
- 1 year plus of hands-on experience in training, fine-tuning, or evaluating LLMs
- Proficiency in machine learning libraries (transformers, datasets).
- Bachelor's or Master's degree in Computer Science, Machine Learning, or related field, or equivalent practical experience.
Preferred Qualifications
- Foundational understanding of RAG architectures and vector-based retrieval systems
- 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.