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.

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