Machine Learning Engineer - LLMs, Agent Systems, and Simulation Tooling, Siri Core Modeling

Join the team redefining what a deeply personal and integrated assistant can be. As part of the Siri organization, you will help shape one of the worlds most widely used AI assistants, powered by our next-generation of Apple Intelligence, with capabilities like personal context understanding and on-screen awareness, built with privacy from the ground up. Your work will have direct, meaningful impact for users across iOS, iPadOS, macOS, watchOS, and visionOS. This is a rare opportunity to build at the intersection of cutting-edge AI and human-centered design, shipping technology that is centered around users and their needs.

Join a pioneering team shaping the future of voice-first, agentic platforms. As a Senior Machine Learning Engineer, you'll help define how next-generation intelligent agents reason, plan, and interact with people through natural voice and multimodal experiences. You will develop the foundations of scalable LLM reasoning systems that will power the next wave of human-AI interaction. We're seeking a senior ML engineer with strong expertise in large language models and agent-based systems to build the core reasoning and simulation capabilities behind a future platform for agentic voice experiences. You will work on advancing how LLMs plan, adapt, and evaluate actions in realistic environments, contributing to the development of reliable and trustworthy AI agents. Your work will focus on developing robust infrastructure and tooling for training, simulation, and evaluation of agentic LLMs. You'll design and run experiments in simulated environments, build scalable evaluation pipelines, and help integrate agent behaviors across client and backend systems. This role is an opportunity to push the boundaries of reasoning, adaptive behavior, and platform architecture for agent-based intelligence. You will collaborate closely with ML scientists, applied researchers, and product engineers to transform early research into deployable systems. Together, we will shape a platform that empowers developers and end-users to build rich, voice-driven AI experiences.Design and implement scalable simulation and evaluation frameworks for LLM-based agents Collaborate with ML researchers to translate novel reasoning approaches into production-grade systems Develop infrastructure for training and experimentation with agentic behaviors Integrate agent reasoning capabilities into client-side and backend environments Drive iteration speed by creating reliable tooling for experimentation, prototyping, and deploymentBachelor's degree in Computer Science, Machine Learning, or related quantitative field, with 3+ years of relevant industry experience Strong skills in Python (preferred) and at least one other programming language Proven experience in ML engineering, including system design, training pipelines, and deployment workflows Deep understanding of agent-based simulation, agentic RAG systems, and LLM evaluation methodologies Ability to balance long-term platform vision with pragmatic short-term delivery in fast-paced environmentsExperience deploying LLM models in research or production contexts Knowledge of adaptive feedback loops, reinforcement learning, or interactive agent design Familiarity with client-backend integration for AI-driven applications MS or PhD in Computer Science, Machine Learning, or a related field