ML Data & Eval Engineer, Siri Agent Modeling

Apple · Location Cupertino · $150,400–$277,600 (stated in the posting)

ML Data & Eval Engineer, Siri Agent Modeling
Cupertino, California, United States
Software and Services
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Summary
Posted:
Oct 15, 2026
Weekly Hours:
40
Role Number:
200689239-0836
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
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 $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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