The AI industry has a dirty secret.
Behind every intelligent application is a mountain of manual effort. Today, many engineering teams are buried in tickets, managing Kubernetes clusters, and constantly tuning inference just to control costs. It’s slow, expensive, and increasingly unsustainable.
This company is building a different approach...
They’re a fast-growing AI infrastructure business, backed at scale, focused on making cloud native AI systems far more autonomous and efficient. Their platform goes beyond monitoring, it actively optimises and adapts infrastructure in real time, helping large organisations run complex ML workloads without constant human intervention.
This isn’t a maintenance role, it’s deeply R&D focused. You’ll take ownership of greenfield problems around scaling modern AI systems, from improving inference efficiency to designing smarter ways of routing and executing workloads across distributed environments. The work touches everything from model performance to cost optimisation at scale.
You’ll be working at the intersection of machine learning and high performance systems, using a modern stack built around Python and leading ML frameworks, alongside high-throughput data systems and cloud-native tooling. The platform runs across multiple cloud providers and is heavily integrated with Kubernetes.
They’re looking for engineers who have spent time in the trenches of ML or data systems, particularly those who have worked on improving inference performance, whether through optimisation, resource efficiency, or system level tuning.
If you’re interested in solving hard scaling problems in a high autonomy environment, it’s worth a conversation.
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