Sr. ML Production Model Automation Engineer, Siri Speech

Apple; Cupertino (CA); On-site; USD 185,000 - 325,000; Full time

Benefits offered by this job

Stock programs

Bonuses/relocation

Medical and dental coverage

Retirement benefits

Discounted products

Tuition reimbursement

Job summary

Apple is seeking an experienced ML Ops engineer to own end-to-end model pipelines and automation across on-device and distributed training platforms. You will enable rapid iteration, staging, rollout and deprecation while building agent-based workflows and tooling for SFT, LoRA, and RL phases.

Ideal candidates bring strong Python/Bash skills, experience with JAX/XLA, TPU, and cloud ML platforms, and a track record in observability and reliability across complex ML systems.

Qualifications

• Bachelor's degree in Computer Science or equivalent.
• Hands-on with JAX, XLA, or large-model training stacks.
• Experience with multi-slice TPU training and cross-region storage.
• Background in MLOps tools: model registries, feature stores, experiment trackers.
• 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

• 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.

Skills

Python

Bash

ML Ops

Observability

Reliability

Cloud ML platforms

Distributed systems

Education

Bachelor's degree in Computer Science or equivalent

Tools

JAX

XLA

TPU

GCS

S3

Kubernetes

Terraform

Job description

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.

Responsibilities

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.

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.

Pay & Benefits

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $184,700 and $324,800, and your base pay will depend on your skills, qualifications, experience, and location.

• Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan.
• You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition.
• Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation.

Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.