Skill

Federated Learning

Data Science, Analytics and AI/ML

Federated learning is a machine learning technique where a model is trained across multiple decentralized devices or servers holding local data samples, without exchanging the raw data itself—only model updates are shared and aggregated centrally. It was popularized by Google for improving predictive text on mobile keyboards while preserving user privacy, and is now used in healthcare, finance, and IoT settings where data privacy or regulatory constraints prevent centralizing sensitive data. ML engineers and privacy researchers use it to balance model performance with data protection.

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Federated Learning in the job market

Last checked September 14, 2026

Open roles: 3
Employers: 3
Median pay: —
Disclose pay: —

Open roles requiring Federated Learning (3)

Development Architect (f/m/d) - AI Foundation Model Training and Serving

SAP

Full-time · Potsdam, DE, 14469

Design and lead the AI infrastructure for a distributed platform enabling foundation model training and deployment across enterprises. This role suits architects with deep expertise in large-scale ML systems who can bridge research, product, and engineering teams while navigating complex federated environments.

Listed on SAP’s careers site · Apply there ↗

AI Technical Lead

NIO

Full-time · San Jose, CA · $192,100 – $249,600

NIO is seeking a technical leader to architect and oversee hybrid AI inference systems that intelligently distribute large language model workloads between edge and cloud infrastructure for autonomous vehicles. This role suits someone with deep expertise in distributed ML systems, model optimization, or AI compiler engineering who can lead a specialized team and drive complex technical decisions across multiple domains.

Listed on NIO’s careers site · Apply there ↗

Staff+ Software Engineer, Privacy

Menlo Ventures Portfolio

Full-time · New York, NY · $405,000 – $485,000

Join Anthropic as a foundational privacy engineer establishing the privacy function for frontier AI systems, designing and implementing privacy-preserving architectures from the ground up. This senior individual contributor role combines privacy engineering, AI safety, and distributed systems work to embed privacy protections into large-scale AI training and inference systems while ensuring regulatory compliance.

Listed on Menlo Ventures Portfolio’s careers site · Apply there ↗

Roles that use Federated Learning

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