Job 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. 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 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

Requirements

Must-haves

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
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

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlpythondockerkubernetesaillm

Platform: apple
Effort: medium