ML Data & Eval Engineer, Siri Agent Modeling

The frontier of AI isnt in the cloud anymore. Its in your pocket. Every model that runs on a users 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 Apples Siri team were building the next generation of agents and deploying them at the largest customer scale in the world. Were 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 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.

As an ML Data u0026 Eval Engineer youll work closely with Siri on-device modeling evaluation and deployment teams and with Apples ML platform compute framework and privacy partners. Youll design build and run the systems that turn data into trained evaluated and deployable on-device models. Youll own your services and data products through every stage: conception design implementation deployment and maintenance.

Your Playground:nnData 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 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 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 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 u0026 Privacy by Design: Build data quality checks lineage and governance tooling that keep our datasets and models trustworthy and consistent with Apples privacy a team that values velocity and pragmatism. We debate we build we measure and we ship. You are an ideal candidate if youve ever been woken up by a failed pipeline or training job and fixed it so it never happens again.n

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

Bachelors degree in Computer Science Engineering or a related discipline or equivalent industry experience.n4 years of industry experience building production data or ML building data pipelines for LLMs such synthetic data generation LLM-as-judge filtering or evaluation setsnExperience with workflow orchestrators such as Airflow Kubeflow or ArgonExperience with model conversion and optimization for on-device or edge deployment (e.g. Core ML quantization pruning)nExperience with data quality lineage and dataset versioning tools and with privacy-preserving data practicesnExperience building tools and services that optimize ML systems beyond model selectionnCollaborative with experience driving large cross-team projects