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About the Role
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 make Siri great in every language.
Responsibilities
- Design, build, and ship the data, models, and systems that bring Siri's AI features to new languages and markets.
- Keep raising quality in the markets we already serve.
- Work closely with Siri's modeling, evaluation, data science, and deployment teams.
- Partner with Apple's Modeling, ML platform, and safety teams.
Requirements
- 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.
Skills
- Python
- LLM
- NLP
- ML
- ASR
- acoustic triggering
- TTS
- agentic AI-assisted coding
- context engineering
- data-driven mindset
- building and debugging production services
- building synthetic data generation pipelines
- large-scale data infrastructure
- ML infrastructure
- LLM evaluation
Experience Level
- Equivalent experience to B.S. or M.S. in Computer Science or related field
Education Level
- B.S. or M.S. in Computer Science or a related field
About the Company
- 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.