Sr. ML Production Model Automation Engineer, Siri Speech

Summary

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 world's 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.

Description

We are the team building products for voice, dictation and other audio products at Apple. These are multimodal models that power Siri on-device speech features, and the next generation of audio experiences across our platforms. Our researchers and modeling engineers train models, iterate on data mixtures spanning conductor backed Siri telemetry to synthetic voice corpora, and stack supervised fine-tuning, LoRA adapter training, and reinforcement learning into pipelines that produce the adapters, tokenizers and detokenizers.

You’ll join a small group of production automation engineers whose mandate is to turn the operational substrate underneath foundation model training into a reliable, observable, self-serve system. The work spans python, shell tooling, cloud platform integration, internal CLI design, and close partnership with the product and research teams you are enabling.

Preferred Qualifications

Bachelors degree in Computer Science or equivalent technical discipline
Hands-on with JAX, XLA, or large-model training stacks or equivalent.
Experience with multi-slice TPU training and cross-region GCS / S3-compatible storage.
Background in MLOps tools: model registries, feature stores, experiment trackers, reward-model serving for RL.
Prior work simplifying onboarding and access provisioning (Apple Access Manager, AWS IAM at scale, or equivalent).
Experience writing Claude Code / agent skills, runbooks, or other LLM-assisted developer tooling.

Minimum Qualifications

Strong software engineering fundamentals; comfortable in Python and Bash, comfortable reading and refactoring large internal codebases.
5+ years experience in Machine Learning Operations.
Production experience with one or more cloud ML platforms (GCP TPU, AWS GPU clusters, Kubernetes-backed training infra) including submitting jobs, debugging schedulers, working around quota systems.
Familiarity with the ML training lifecycle: data preprocessing pipelines, distributed training, checkpoint formats, multi-slice / multi-region considerations.
Experience with infrastructure-as-code, CLI tool design, and developer ergonomics. You've shipped tools that other engineers actually use.
Bias toward observability and reliability.
Comfortable working across team boundaries: you'll partner with researchers, product and infra teams.