Senior Machine Learning Systems Engineer, Ads ML Experience Platform
Builds scalable experimentation, training, orchestration, and agentic AI infrastructure that accelerates Reddit’s Ads ML lifecycle. Requires 5+ years in infrastructure or distributed systems and production ML platform experience.
About the job
Responsibilities
- Design and build large-scale offline machine learning experimentation platforms for reproducible research, model development, evaluation, and promotion workflows.
- Develop production-grade training orchestration frameworks for 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 machine learning 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 for autonomous and human-in-the-loop workflows, including multi-agent orchestration, memory and context systems, and scalable workflow infrastructure.
Requirements
- 5+ years of experience in infrastructure/platform engineering or large-scale distributed systems.
- 2+ years of hands-on experience building and operating production machine learning 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 machine learning experimentation platforms, model registries, experiment tracking systems, or training orchestration frameworks.
Nice-to-Haves
- Experience building and operating agentic AI systems, including multi-agent orchestration, autonomous workflows, and agent communication or runtime frameworks such as MCP and A2A.
- Experience running end-to-end model development and iteration cycles at scale.
Compensation and Benefits
- Base salary range: $216,700–$303,400 USD.
- Equity in the form of restricted stock units; some positions may also be eligible for commission.
- Comprehensive healthcare benefits and income replacement programs.
- 401(k) with employer match.
- Global benefits supporting workspace, professional development, caregiving, and family planning.
- Gender-affirming care.
- Mental health and coaching benefits.
- Flexible vacation and paid volunteer time off.
- Generous paid parental leave.
Skills
Machine Learning, Distributed Systems, ML Infrastructure, Python, Spark, Flink, Ray, Kubeflow, Argo, Airflow, Hyperparameter Optimization, Model Registries, Experiment Tracking, Agentic AI, Multi-Agent Orchestration
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