Machine Learning Engineer - Agentic AI

The VCV organization has pioneered human-centric, real-time technologies such as Face ID, FaceKit, and gaze and hand gesture control, transforming how millions of people interact with their devices by balancing research and product requirements to deliver Apple-quality experiences through full-stack innovation and close collaboration across HW, SW, and AI teams. Our team develops and integrates large language models (LLMs) and other cutting-edge AI techniques to enhance the autonomy and intelligence of our agentic systems.

We are looking for an experienced engineer to design and build end-to-end agentic systems. This role focuses on developing systems that integrate large language models, tools, and data systems to execute multi-step workflows in production environments. You will work across the full stack to translate complex problems into reliable, scalable systems. You will design and implement agentic systems built around large language models (LLMs) that extend beyond traditional machine learning pipelines. The work will require making tradeoffs between latency, cost, accuracy, and controllability, including decisions between deterministic pipelines and adaptive, LLM-driven approaches within agentic system design.Build systems that combine models, tools, and data into cohesive, agentic workflows capable of executing multi-step tasks. This includes designing system behaviors such as planning, tool use, structured outputs, and failure handling. Develop infrastructure for evaluating and improving agentic system performance, including quality, reliability, and cost, and build monitoring and observability systems to understand behavior in production. Integrate LLMs with internal and external tools, enabling agentic systems to retrieve context, call APIs, and execute actions as part of end-to-end workflows. Collaborate with cross-functional teams to translate product requirements into scalable agentic systems, and continuously improve system performance through iteration and evaluation.Proven knowledge of cutting-edge agentic systems. Demonstrated experience designing and shipping agentic systems in production environments. Strong proficiency with LLM-assisted coding, including using AI tools to design, implement, and iterate on complex systems. Proven ability to design end-to-end systems, making architectural decisions across multiple components (e.g., services, data pipelines, integrations).Demonstrated track record of building and shipping agentic or LLM-based products, with visible portfolio (e.g., apps, open-source projects), or recognized contributions such as publications in top-tier conferences or impactful technical work. Experience developing and deploying computer vision and machine learning (CVML) systems in production environments. Strong system design experience, including defining and evolving architecture for complex, multi-component systems. Experience taking products from concept to launch, including delivering user-facing applications at scale or in real-world environments.