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.\\n\\nContribute to model development and fine-tuning workflows for generative AI features. \\nDesign and evaluate retrieval strategies for grounding large models in product-relevant data. \\nPrototype and benchmark multi-agent collaboration systems for structured reasoning tasks. \\nPartner 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!
5+ years of hands-on experience in ML, backend engineering, data engineering \n1-2 years of hands-on experience in training, fine-tuning, or evaluating LLMs\nFoundational understanding of RAG architectures and vector-based retrieval systems\nStrong experience partnering with business and engineering team to deliver AI solutions\nBachelor's or Master's degree in Computer Science, Machine Learning, or related field, or equivalent practical experience.
Exposure to multi-agent orchestration frameworks in Rust and Python. \nFamiliarity with modern deep learning frameworks such as PyTorch, TensorFlow, or JAX. \nExperience with data preprocessing, tokenization, and pipeline automation. \nProficiency in machine learning libraries (transformers, datasets). \nStrong problem-solving and collaboration skills, with the ability to learn quickly and adapt to production-grade systems. \nExperience working with Multi-modal LLMs to enable Voice capabilities is a plus or prior experience with STT, TTS systems.\nExperience with deploying to cloud environments (AWS, Google Cloud Platform, on-remote hybrid) is required. \n
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
- Dice Id: 90733111
- Position Id: 713bda819eff42498ed6746fa8a70d6d
- Posted 5 hours ago
Never miss an opportunity! Create an alert based on the job you applied for.