Applied AI Engineer – Global Siri

Siri AI talks with hundreds of millions of people in dozens of languages, and each market expects it to understand its language, culture, and context as well as a local would. It takes the right data, careful evaluation, models built for each locale, and systems that can do all of this across many markets at once.

The Global Siri team builds the next generation of Siri's AI agents for users around the world. We're looking for a strong AI engineer who works across the whole ML lifecycle (data, modeling, evaluation, and deployment) and uses agentic tools every day to get more productive to make Siri great in every language.

As an applied AI Engineer on Global Siri, you'll work closely with Siri's modeling, evaluation, data science, and deployment teams. You'll also partner with Apple's Modeling, ML platform, and safety teams. You'll design, build, and ship the data, models, and systems that bring Siri's AI features to new languages and markets and keep raising quality in the markets we already serve.

B.S. or M.S. in Computer Science or a related field, or equivalent experience Strong software engineering fundamentals and proficiency in Python Hands-on experience across the ML lifecycle (data, training, evaluation, deployment). Strong understanding of LLM, NLP, ML and their application.

Hands-on experience with ASR, acoustic triggering, or TTS in international locales, including accents, code-switching, low-resource languages, and locale-specific metrics Extensive hands-on experience with agentic AI-assisted coding and context engineering An analytical, data-driven mindset and experience.

Experience building and debugging production services or data products end to end Excellent communication skills and the ability to work across teams in an English-speaking environment Strong written and verbal communication skills

Agentic coding and GenAI as your daily way of working, including building your own agents or workflows that automate real engineering and data tasks Experience building synthetic data generation pipelines and using them to launch features or improve product metrics, especially where real data is scarce Experience with large-scale data or ML infrastructure (e.g.

Spark, Kubernetes) and LLM evaluation, such as LLM-as-judge A track record of shipping user-facing ML features in multiple languages or markets