Role overview
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.
**Description**
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.
","responsibilities":"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 deployment
**Preferred Qualifications**
Experience 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
**Minimum Qualifications**
Bachelor’s degree in Computer Science, Machine Learning, or related quantitative field, with 4+ 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 environments
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant .