Quantitative Developer - London (Front Office Build-Out) - Early Tech Hire - Multi-Strategy Hedge Fund - Up to £450k TC

Most quant dev roles at this level offer good money and interesting problems. This one also offers something rarer: the chance to be the person who builds it.

A well-capitalised, multi-strategy hedge fund is establishing its London engineering presence. The US platform is mature and battle-tested. London is the next chapter and this hire is the foundation of it.

You'll report directly to the Head of Development, work shoulder-to-shoulder with portfolio managers and traders, and own the technical relationship with the European desks. No ticket queue. No three-sprint planning cycle before you can touch production. The PMs come directly to you, and what you ship gets used the same day.

The Technical Problem

The core mandate is a significant one: modernising trading and risk infrastructure that has grown organically over time. Much of it is batch-driven, end-of-day PnL, overnight risk runs, T+1 reporting in an environment where desks increasingly need intraday visibility and real-time feedback loops.

Your job is to change that. Concretely:

• Real-Time PnL & Risk Systems - Rebuild core PnL and risk infrastructure away from batch processing toward event-driven, low-latency architectures. This means rethinking data flows from market data ingestion through to risk aggregation: handling live price updates, position changes, and exposure calculations with the kind of latency that actually matters to a PM mid-session.
• Pricing & Risk Infrastructure - Design and implement scenario analysis, stress testing, and margin replication tooling. This involves working across asset classes: equities, rates, FX, derivatives and building systems flexible enough to handle both systematic and discretionary desk requirements without bespoke one-offs for every desk.
• Research & Analytics Platform - Own the design and build of a firm-wide, service-oriented research, back-testing and analytics platform serving both quant researchers and discretionary PMs. This is a greenfield opportunity: define the architecture, choose the abstractions, and build something that scales across the fund rather than accretes desk by desk. You'll work directly with quants to understand their framework needs and translate them into infrastructure that doesn't get in their way.
• Data Pipelines & PM Tooling - Build and maintain high-quality pipelines over large-scale, multi-asset time-series data: pricing, reference data, alternative data feeds. Develop interactive tooling that lets PMs and researchers interrogate portfolios, run scenarios, and explore signals without filing a request to engineering.
• Signal Productionisation - Work alongside quants to take trading signals from research notebooks to production systems: handling the unglamorous but critical work of data quality, scheduling, monitoring, and alerting that the difference between a backtest and something a desk trusts with real risk.

The Stack & Environment

Python is the primary language: NumPy, Pandas, and the wider scientific stack are day-to-day tools. You'll need strong software engineering fundamentals to go alongside the quant fluency: clean abstractions, testable code, systems that can be handed off and extended rather than held together by institutional knowledge.

SQL proficiency is expected. Exposure to C# or R is useful but not essential. Experience with time-series databases, message queues, or stream-processing frameworks (Kafka, Redis Streams, similar) is a genuine advantage given the direction of the infrastructure.

The problems you'll face are classic high-performance data engineering challenges: throughput vs. latency trade-offs, schema evolution across long-lived time-series, consistency guarantees in distributed risk aggregation, and making research environments reproducible enough that a backtest from eighteen months ago can actually be trusted. You'll also be expected to monitor and tune infrastructure for performance, efficiency and cost, this isn't a build-and-throw-it-over-the-wall environment.

What They're Looking For

• 5+ years in a front-office or trading-aligned engineering role: buy-side, sell-side, or a trading technology firm with genuine market exposure
• Strong Python with real software engineering rigour: not just scripting, but production systems with proper testing, observability and maintainability
• Market and time-series data experience: you understand the quirks of financial data at scale: corporate actions, data gaps, survivorship bias, vendor inconsistencies
• Real-time or event-driven systems: experience with low-latency architectures or stream processing is highly valued
• Stakeholder fluency: you can sit with a PM, understand what they actually need (which is rarely what they first ask for), and translate that into a technical spec without losing anything in translation
• Quantitative background: CS, Maths, Physics, Engineering or equivalent. You don't need to be a quant, but you need to think like one

Why This, Over Your Current Role

The London team is early-stage by design. That's not a warning, it's the point. You'll have genuine input into architecture decisions, technology choices, and how the engineering-to-desk relationship works from day one. You won't be inheriting someone else's framework and optimising around the edges.

At the same time, you're not starting from scratch in a vacuum. There's an established US engineering platform to draw on, a mature risk and trading infrastructure to learn from, and a fund with real AUM, real trading, and real stakes. The support is there. The bureaucracy isn't.

The compensation reflects the seniority of the mandate — up to £450k total comp for the right profile. But the more interesting question, for the kind of engineer this role is designed for, is whether the work itself is worth moving for.

If you're technically strong, comfortable with ambiguity, and ready to own something meaningful rather than contribute to something managed, this is worth a conversation.