Sr. ML Production Model Automation Engineer, Siri Speech

Join the team redefining what a deeply personal and integrated assistant can part of the Siri organization you will help shape one of the worlds 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 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.

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 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.

Own the end-to-end model lifecycle building model pipelines integrating with other Apple frameworks to enable rapid model iteration staging promotion production rollout and and operate agent-based automation pipelines for ML models where agents own decision logic at each gate and humans approve only at defined escalation pointsnDevelop multi-agent workflows using LLM-native tooling for on-device evaluation regression triage release readiness decisions and automated root cause the launch tooling to build and improve the shell scripts and CLI commands that turn a config-name and a dataset into a running training job across SFT LoRA adapter and RL phases.

Strong software engineering fundamentals; comfortable in Python and Bash comfortable reading and refactoring large internal codebases.n5 years experience in Machine Learning 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 with the ML training lifecycle: data preprocessing pipelines distributed training checkpoint formats multi-slice / multi-region with infrastructure-as-code CLI tool design and developer ergonomics. Youve shipped tools that other engineers actually toward observability and working across team boundaries: youll partner with researchers product and infra teams.

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