Join the team redefining what a deeply personal and integrated assistant can be. \\n\\nAs 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.\\n\\nThis 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.\n\nYou'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.
Strong software engineering fundamentals; comfortable in Python and Bash, comfortable reading and refactoring large internal codebases.\n5+ years experience in Machine Learning Operations.\nProduction experience with one or more cloud ML platforms (Google Cloud Platform TPU, AWS GPU clusters, Kubernetes-backed training infra) including submitting jobs, debugging schedulers, working around quota systems.\nFamiliarity with the ML training lifecycle: data preprocessing pipelines, distributed training, checkpoint formats, multi-slice / multi-region considerations.\nExperience with infrastructure-as-code, CLI tool design, and developer ergonomics. You've shipped tools that other engineers actually use.\nBias toward observability and reliability.\nComfortable working across team boundaries: you'll partner with researchers, product and infra teams.
Bachelors degree in Computer Science or equivalent technical discipline\nHands-on with JAX, XLA, or large-model training stacks or equivalent.\nExperience with multi-slice TPU training and cross-region GCS / S3-compatible storage.\nBackground in MLOps tools: model registries, feature stores, experiment trackers, reward-model serving for RL.\nPrior 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.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
- Dice Id: 90733111
- Position Id: d6c1575d1c21d66dfbb59fe7f7ca7443
- Posted 14 hours ago
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