Company
Scaleout builds software infrastructure for machine learning in distributed environments. Our platform, Scaleout Edge, lets organisations train and manage models across decentralised systems without moving sensitive data. Data stays where it belongs while models improve collectively.
Mission
AI systems are increasingly built on data that cannot be centralised. Medical records, industrial data, defence systems and connected devices all operate in environments where data must remain local.
Our mission is to make it possible to train and operate AI across these environments without moving sensitive data. By enabling organisations to collaborate on machine learning while retaining full control over their data, we help build more secure, sovereign, resilient and trustworthy AI systems.
Scaleout was founded in 2018 by researchers from Uppsala University working on large-scale distributed systems and scientific computing.
Team
Scaleout is a team of data scientists, machine learning engineers, software engineers, and entrepreneurs. The team combines academic research and industry experience, with backgrounds at Google, Saab, BCG, Oracle and MySQL.
Co-Founder / CEO
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Co-Founder / CCO
Head of Engineering
Senior FL Engineer
CISO
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MLOps Engineer
Senior ML Researcher
ML Engineer
Principal Software Developer
Research Engineer
Operations
Careers
We're always looking for people who want to build infrastructure for AI in the world's most demanding environments.
Explore five research projects in federated learning, efficient edge AI and AI security.
Investigate how dynamic low-bit training can reduce communication costs in federated learning, adapting precision to network conditions and client data while preserving model accuracy.
Read project description (PDF)Explore how context-aware routing, quantization and pruning can reduce the memory and compute costs of vision models on resource-constrained edge devices.
Read project description (PDF)Develop adaptive sub-networks tailored to each device’s capacity, enabling devices with different compute and memory resources to train a shared model collaboratively.
Read project description (PDF)Study how vision models can adapt to new domains by adding or swapping experts, using context to guide routing while balancing new learning with knowledge retention.
Read project description (PDF)Evaluate how active gradient inversion attacks can expose training images in federated object detection, and investigate client-side defenses and the trade-offs between information leakage and detection accuracy.
Read project description (PDF)To apply, send your CV, academic transcripts and a brief motivation statement referencing relevant project or coursework experience to viktor@scaleoutsystems.com. See each project description for prerequisites and research questions.