About the role

Hyperion Quantitative Strategies is an AI-native systematic fund built end to end by its own engineering team. Kubernetes on Google Cloud, Terraform-managed infrastructure, automated pipelines pulling market data across equities, options, index options, volatility and crypto at minute and daily resolution, a proprietary backtesting engine, and a live trading book. The fund is already in discussions that would take it to several hundred million dollars in assets under management, and the ambition is a firm built in the spirit of Renaissance Technologies, with AI core to how it operates rather than a layer sitting on top.

The engineering bench is deliberately small and senior. One engineer deep in backtesting and infrastructure, one in agentic systems and the data lake, one in data engineering — and the CTO owning the quantitative layer above them. This role sits across the whole platform: cloud architecture, data pipelines, production reliability and live-system ownership, in one senior individual contributor seat. Infrastructure is the floor, not the ceiling. You design the systems, you run them, and the calls about where the platform goes next are yours to make.

You will report directly to the CTO and co-founder. Copenhagen is the preferred base; strong candidates in CET-compatible European time zones are considered. Hyperion is built inside the Bifrost Studios portfolio, and Bifrost Studios is running this search.

What you'll do

• Own and evolve the Kubernetes platform on Google Cloud (GKE) through Terraform, with infrastructure as code treated as an engineering discipline rather than a collection of scripts.
• Build and operate the pipelines that ingest market data from multiple vendors — normalisation, validation and strict format compliance included — so the trading engine always has numbers it can rely on.
• Own deployment, monitoring, alerting and incident response for the live trading systems, so problems in the running book surface early and get resolved fast.
• Own the relational and analytical databases behind trading and research: schema design, performance, operations, and fault-tolerant, high-availability architecture where it earns its place.
• Steward the integrations with external data providers and broker connectivity, and treat the proprietary data as a long-term asset — a number pulled two years from now should still be safe to trade on.
• Cut operational toil through automation, so the platform needs progressively less manual intervention as it matures.