ML Infrastructure Engineer - Multimodal Training Tools, SIML

Are you passionate about Generative AI? Are you interested in working on groundbreaking generative modeling technologies to enrich billions of people? We are the Intelligence System Experience (ISE) team within Apple's software organization. The team operates at the intersection of multimodal machine learning and system experiences. Our multidisciplinary ML teams focus on a broad spectrum of areas, including Visual Generative Foundation Models, Multimodal Understanding, Visual Understanding of People, Text, Handwriting, and Scenes, Personalization, Knowledge Extraction, Conversation Analysis, Behavioral Modeling for Proactive Suggestions, and Privacy-Preserving Learning. These innovations form the foundation of the seamless, intelligent experiences our users enjoy every day.\\n\\nWe are seeking engineers experienced in building tools for training, adapting and deploying large-scale generative models. You will be working alongside a cross functional team of engineers who own ML infrastructure & algorithms, data scientists, designers, safety and UX engineers.

In this role you will have a deep expertise in ML tooling, with a passion to empower engineers across the ML stack.\n\nResponsibilities include:\n\n- Contributing towards tools for large generative model training including diffusion & autoregressive workflows\n\n- Tools for efficient inference and hosting of models for experimentation and human feedback\n\n- Tooling for model representation and efficient deployment on multiple HW targets incl. Apple Silicon \n\n- Benchmarking, Analysing and Improving training and inference performance\n\n- Integrating efficient data loading strategies and auto-eval workflows \n\n- CI/CD of base training workstreams

Bachelors, Masters, or PhD in Electrical Engineering/Computer Science or a related field (mathematics, physics or computer engineering), with a focus on machine learning; or comparable professional experience\nExperienced in training / adapting LLM and Diffusion models\nAdvanced Fluency in PyTorch \nExcellent programming skills and experience contributing software to large projects\nExperience with distributed training of large models

Strong ML Fundamentals\nExperience working with large cross-functional and diverse teams.