Senior Machine Learning Systems Engineer

What You’ll Do

• 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.

What You Bring

• 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.