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# ML Data & Eval Engineer, Siri Agent Modeling

- **Company:** Apple Inc.
- **Location:** Cupertino, CA, United States
- **Experience:** Experienced
- **Salary:** $150,400.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Big Data, Cloud Computing, Data Validation, Data Files, Extract Transform Load (ETL), Data Structures, Software Debugging, Distributed Systems, Revision Control Systems, Python (Programming Language), Machine Learning, Operational Databases, Azure Machine Learning, Management of Software Versions, Privacy Controls, Pytorch, Large Language Models, Apache Spark, Siri, Discretization, Kubernetes, Information Technology, Machine Learning Operations, Data Pipelines, Docker, Data Generation
- **Published:** October 16, 2026
- **Apply:** https://www.jobmonkeyjobs.com/career/28112174/Ml-Data-Eval-Engineer-Siri-Agent-Modeling-California-Cupertino-7413

## About the Role

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

## 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.