AI Federated Edge Model Aggregation Engineer

About the role

Responsibilities

• Design federated aggregation protocols implementing secure aggregation and differential privacy mechanisms
• Build non-IID handling strategies addressing systematic data distribution differences across device populations
• Implement participation scheduling for intermittently connected edge devices
• Develop convergence monitoring tracking global model quality improvement across federated rounds
• Create device contribution weighting accounting for data quality and quantity variation
• Build privacy accounting system tracking cumulative privacy budget consumption across training rounds

Requirements

• 6+ years in ML systems with federated learning or distributed model training experience
• Deep understanding of federated aggregation algorithms, differential privacy, and secure aggregation
• Strong Python and PyTorch skills with distributed systems design experience
• Familiarity with edge deployment constraints and intermittent connectivity patterns
• Research or production experience with federated learning at scale

Benefits

• Fully remote with flexible schedule
• Comprehensive health benefits
• $4,000 annual professional development budget
• Equity participation
• Conference and publication budget

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