Sr. ML Production Model Automation Engineer, Siri Speech

Apple • Cupertino, CA

1d

About The Position

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. 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.

Requirements

• 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.
• Bachelors degree in Computer Science or equivalent technical discipline.

Nice To Haves

• 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.

Responsibilities

• Turn the operational substrate underneath foundation model training into a reliable, observable, self-serve system.
• 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 deprecation.
• Design and operate agent-based automation pipelines for ML models where agents own decision logic at each gate and humans approve only at defined escalation points.
• Develop multi-agent workflows using LLM-native tooling for on-device evaluation, regression triage, release readiness decisions, and automated root cause analysis.
• Own 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.
• Partner with researchers, product and infra teams.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior