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Location: Central London (On-site) | Type: Full-time | Role: Founding Engineering Hire
Numi is partnering with a stealth, next-generation systematic trading and AI research lab to find their founding engineering hire. They combine machine learning, neuroscience-inspired architectures, and high-performance engineering to build decision engines that operate in live markets, where reliability and precision translate directly into performance.
The firm is backed by Tier-1 Silicon Valley and European venture funds. They are already live, running multiple strategies in production across venues on self-built infrastructure that handles real-time data ingestion, low-latency messaging, research compute, and monitoring. What they do not yet have is a dedicated engineer who owns that platform end-to-end. That is this role. You will take what is already working, make it bulletproof, and scale it into a genuine infrastructure advantage.
The Team
A small, dense, high-pedigree team consisting of senior ex-professionals from Citadel, XTX, Brevan Howard, and Citi, alongside graduates and postgraduates from Oxford, Cambridge, Princeton, and Cornell. This is a research-lab culture: high trust, high autonomy, low bureaucracy, and a very high bar.
The Role
You will work directly with the founders, researchers, and traders to design and run the internal platform that powers live trading and research. Your mandate is simple: build an infrastructure advantage.
What You'll Own
- Production Infrastructure: Multi-region cloud and latency-sensitive environments.
- Reliable Systems: Live trading, multi-venue market data ingestion, and research compute.
- Deployment Pipelines: CI/CD that lets the team ship strategy and model changes quickly and safely.
- Observability: End-to-end monitoring across data quality, execution, strategy, and infrastructure.
- Resilience: Failover, disaster recovery, and operational readiness for systems where downtime has an immediate financial cost.
- Research-to-Prod Pipeline: Bridging the route from research prototype to production service with rigorous testing, monitoring, and maintainability.
- Security Architecture: IAM, networking, secrets management, and access control.
- ML-Serving Infrastructure: Low-latency model serving, feature pipelines, and experiment tooling for proprietary research models.
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What the First Six Months Might Include
(Note: These are illustrative; you will help set the actual priorities.)
- Measurably improving the performance of live strategies (lower latency, tighter execution, better fill quality).
- Hunting "infra alpha"—finding competitive edges derived purely from engineering excellence in data freshness, latency, execution, and routing.
- Building a fast research and backtesting environment, plus the tooling to take new strategies from idea to live with far less friction.
- Establishing a CI/CD pipeline that turns a researcher's validated change into a safe production deploy in minutes.
- Hardening the multi-region footprint with supervised services, automated recovery, and zero single points of failure.
- Creating a single-pane-of-glass view of system health across every venue and service.
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Likely Background
You might come from Jane Street, D. E. Shaw, Two Sigma, Hudson River Trading, Optiver, XTX, Citadel, an exchange/market-infrastructure business, or a top infrastructure team at a tier-one technology company. What matters more than the logo: you have built and run production systems where downtime has a real cost, and you have supported researchers or traders before.
Signals They Look For
- Experience: Building critical production systems with real consequences for failure.
- Reliability: Strong reliability instincts under pressure.
- Engineering Quality: High standards for engineering quality and operational discipline.
- Decision Making: Comfort making decisions with incomplete information and moving quickly.
- Ownership: A desire for true ownership, not just clearing a ticket queue.
Technical Depth
- Core Fundamentals: Deep Linux and networking knowledge.
- Cloud: Strong cloud experience, ideally AWS (compute, networking, IAM, storage, observability).
- Languages: Strong Python; experience with C++, Rust, or Go for latency-critical paths is a major plus.
- Infrastructure as Code: Terraform or equivalent.
- Release Engineering: Robust CI/CD and release pipelines.
- Data Systems: PostgreSQL / TimescaleDB, Kafka, Redis, ClickHouse, and low-latency messaging.
- Monitoring: Grafana, Prometheus, Loki, or similar.
- Mindset: A security-first approach to infrastructure design.
- Bonus: MLOps experience (model serving, feature stores, low-latency inference).
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