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Senior Machine Learning Systems Engineer, Ads ML Experience Platform
Rol remoto de Ads Engineering con fit claro de ubicación del candidato.
PublicadoAgregado recientemente
Países elegibles1 país aceptado
Señal de senioritySenior
Modelo de trabajoRemoto
Ubicaciones aceptadas para candidatos
Estados Unidos
Resumen del rol
Senior Machine Learning Systems Engineer, Ads ML Experience Platform
Requisitos y responsabilidades
Contenido del rol extraído en secciones para revisar más rápido.
Details
- Design and build large-scale offline ML experimentation platforms that enable reproducible research, model development, evaluation, and promotion workflows.
- Develop production-grade training orchestration frameworks supporting distributed training, hyperparameter optimization, model evaluation, and automated retraining.
- Build infrastructure for experiment tracking, metadata management, lineage, artifact versioning, model registries, and reproducibility.
- Partner with ML engineers and researchers to improve experimentation velocity and operational efficiency.
- Build automated workflows for model promotion, rollback, compliance validation, and continuous evaluation.
- Design and build an agentic AI execution platform supporting autonomous and human-in-the-loop workflows, including multi-agent orchestration, memory/context systems, and scalable workflow infrastructure.
- 5+ years in infrastructure/platform engineering or large-scale distributed systems.
- 2+ years of hands-on experience building and operating production ML infrastructure, developer SDKs, platform APIs, or self-service AI tooling.
- Experience building workflow orchestration systems, developer platforms, or large-scale automation frameworks.
- Experience with distributed data processing systems such as Spark, Flink, Ray, or equivalent technologies.
- Experience with modern orchestration and workflow technologies such as Kubeflow, Argo, Airflow, or similar frameworks.
- Experience building offline ML experimentation platforms, model registries, experiment tracking systems, or training orchestration frameworks.
- Experience building and operating agentic AI systems, including multi-agent orchestration, autonomous workflows, and agent communication/runtime frameworks (e.g., MCP, A2A, and orchestration systems) is a strong plus
- Experience running end-to-end model development and iteration cycles at scale is a plus
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