ML Data & Eval Engineer, Siri Agent Modeling

Apple Inc.; Cupertino (CA); On-site; USD 150,000 - 278,000; Full time

Benefits offered by this job

Medical & dental

Retirement benefits

Employee stock programs

Tuition reimbursement

Job summary

Apple Inc. is seeking an ML Data & Eval Engineer to join the Siri team in Cupertino. You will design, build, and operate data pipelines and evaluation systems for on-device models, spanning data curation, training, deployment, and maintenance.

You will own services end-to-end, collaborating with ML platform, compute, privacy, and tools teams while advancing end-to-end ML lifecycle on Silicon.

Qualifications

• Strong CS background in algorithms, data structures, and distributed systems.
• Proficiency in Python; hands-on with Docker, Kubernetes, and cloud infra.
• Experience building large-scale data pipelines (e.g. Spark, Ray) or distributed ML infra.
• Familiarity with ML lifecycle (training, evaluation, deployment) and frameworks such as PyTorch or JAX.
• Experience building, deploying, and debugging production services and data products end to end.
• Experience with agentic AI-assisted coding.

Responsibilities

• Your Playground: Build scalable pipelines for curating, filtering, deduplicating, augmenting, and versioning the data behind our LLMs and agents.
• Evaluation at Scale: Build systems that run large evaluation suites for agentic models, including sandboxed environments for tool use and multi-step tasks.
• Path to Device: Build pipelines that take models from training to Apple silicon, including conversion, compression and quantization workflows, on-device benchmarking, and regression tracking.
• Velocity at Scale: Find and remove bottlenecks across the ML lifecycle, including data loading, experiment tracking, and artifact management.
• Quality & Privacy by Design: Build data quality checks, lineage, and governance tooling that keep datasets and models trustworthy and consistent with Apple's privacy principles.
• We’re a team that values velocity and pragmatism. We debate, we build, we measure, and we ship.

Skills

Python

Docker

Kubernetes

Distributed systems

Spark

Ray

PyTorch

JAX

Production services

Agentic AI

Cloud infrastructure

Education

Bachelor's degree in CS/Engineering

Equivalent industry experience

Tools

Airflow

Kubeflow

Argo

Core ML

Quantization

Pruning

Dataset versioning tools

Job description

Cupertino, California, United States Software and Services

The frontier of AI isn't in the cloud anymore. It's in your pocket. Every model that runs on a user's device depends on a lot of systems behind it: data pipelines that decide what it learns, infrastructure that trains and evaluates it at scale, and tooling that gets it efficiently onto Apple silicon.At Apple's Siri team, we're building the next generation of agents and deploying them at the largest customer scale in the world. We're looking for generalist engineers who like working across the whole ML lifecycle. You might build a data pipeline one quarter and a training platform the next, wherever the biggest bottleneck is.This role is for builders who want end-to-end ownership of the data and infrastructure behind frontier on-device ML, from raw data to silicon.

Description

As an ML Data & Eval Engineer, you'll work closely with Siri on-device modeling, evaluation, and deployment teams, and with Apple's ML platform, compute, framework, and privacy partners. You'll design, build, and run the systems that turn data into trained, evaluated, and deployable on-device models. You'll own your services and data products through every stage: conception, design, implementation, deployment, and maintenance.

Responsibilities

• Your Playground:
• Data as a Product: Build scalable pipelines for curating, filtering, deduplicating, augmenting, and versioning the data behind our LLMs and agents. This includes fine-tuning sets, preference data, tool-use trajectories, evaluation sets, and synthetic data generated by models.
• Evaluation at Scale: Build the systems that run large evaluation suites for agentic models, including sandboxed environments for tool use and multi-step tasks, so every checkpoint gets measured quickly and consistently.
• Path to Device: Build the pipelines that take models from training to Apple silicon, including conversion, compression and quantization workflows, on-device benchmarking, and regression tracking, so every candidate model can be checked against real device constraints.
• Velocity at Scale: Find and remove bottlenecks across the ML lifecycle, including data loading, experiment tracking, and artifact management, so modelers spend their time on models and not on plumbing.
• Quality & Privacy by Design: Build data quality checks, lineage, and governance tooling that keep our datasets and models trustworthy and consistent with Apple's privacy principles.
• We're a team that values velocity and pragmatism. We debate, we build, we measure, and we ship. You are an ideal candidate if you've ever been woken up by a failed pipeline or training job and fixed it so it never happens again.

Minimum Qualifications

• Strong computer science background in algorithms, data structures, and distributed systems design
• Strong proficiency in Python, plus hands-on experience with Docker, Kubernetes, and cloud infrastructure
• Experience building large-scale data processing pipelines (e.g. Spark, Ray) or distributed ML infrastructure
• Familiarity with the ML model lifecycle (training, evaluation, deployment) and frameworks such as PyTorch or JAX
• Experience building, deploying, and debugging production services and data products end to end
• Experience with agentic AI-assisted coding

Preferred Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related discipline, or equivalent industry experience.
• 4+ years of industry experience building production data or ML infrastructure.
• Experience building data pipelines for LLMs, such synthetic data generation, LLM-as-judge filtering, or evaluation sets
• Experience with workflow orchestrators such as Airflow, Kubeflow, or Argo
• Experience with model conversion and optimization for on-device or edge deployment (e.g. Core ML, quantization, pruning)
• Experience with data quality, lineage, and dataset versioning tools, and with privacy-preserving data practices
• Experience building tools and services that optimize ML systems beyond model selection
• Collaborative, with experience driving large cross-team projects

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 $150,400 and $277,600, 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.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant

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Apple accepts applications to this posting on an ongoing basis.