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ML Data & Eval Engineer, Siri Agent Modeling

Apple· Software and Services
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Cupertino, California$150.4k - $277.6kFull-timePosted 1w ago

About the role

Role focusing on end-to-end data and infrastructure for on-device ML agents, from raw data to silicon. Build and operate data pipelines, evaluation systems, and deployment workflows for Siri’s on-device models, with ownership across the ML lifecycle.

Responsibilities

  • Build scalable pipelines for curating, filtering, deduplicating, augmenting, and versioning data behind LLMs and agents (fine-tuning sets, preference data, tool-use trajectories, evaluation sets, synthetic data).
  • Develop systems that run large evaluation suites for agentic models, including sandboxed environments for tool use and multi-step tasks, ensuring quick and consistent measurement at scale.
  • Create pipelines to move models from training to Apple silicon, including conversion, compression and quantization workflows, on-device benchmarking, and regression tracking.
  • Identify and remove bottlenecks across the ML lifecycle (data loading, experiment tracking, artifact management) to maximize researcher productivity.
  • Build data quality checks, lineage, and governance tooling aligned with privacy principles.
  • Collaborate across Siri on-device modeling, evaluation, deployment teams, and Apple’s ML platform/infra/privacy partners.

Qualifications

  • Strong CS background in algorithms, data structures, and distributed systems design
  • Strong proficiency in Python; 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 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
  • Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent industry experience
  • 4+ years of industry experience building production data or ML infrastructure
  • Experience building data pipelines for LLMs (synthetic data generation, LLM-as-judge filtering, evaluation sets)
  • Experience with workflow orchestrators (Airflow, Kubeflow, Argo)
  • Experience with model conversion and optimization for on-device/edge deployment (e.g., Core ML, quantization, pruning)
  • Experience with data quality, lineage, dataset versioning tools, and privacy-preserving data practices
  • Experience building tools/services that optimize ML systems beyond model selection
  • Collaborative, cross-team project leadership

Skills

Required:PythonDockerKubernetesPyTorch/JAX
Nice to have:SparkRayCore ML

Benefits

  • Comprehensive medical and dental coverage
  • Retirement benefits
  • Discounted products and free services
  • Tuition reimbursement for education-related expenses
  • Discretionary bonuses or commissions (possible)
  • Relocation assistance (possible)
  • Stock programs eligibility (RSUs)
  • Employee stock purchase plan eligibility
Last updated Oct 16, 2026. Interested? Apply on the company site.

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