Engineer - Founding Team

We are partnering with a technology company founded by industry veterans with deep expertise in enterprise architecture and quantitative systems. We are building their core engineering team to turn complex business and quantitative ideas into working software. The work spans understanding unfamiliar domains, designing systems, investigating difficult problems, and delivering reliable solutions.

We value engineers who ask precise questions, reason from fundamentals, explore beyond familiar tools, and develop their own informed view. We actively use AI in research and development. You should be able to direct these tools, understand the resulting work, and verify that it solves the problem.

​The opportunity :

You will work directly with decision makers and domain experts in a small team with a flat structure. The work spans problem definition, system design, implementation, investigation and production operation.

The company is transitioning an in-house trading and quantitative research platform into an extensible, multi-tenant software platform with real-time analytics, dynamic strategy deployment, and agentic capabilities. You will design both ultra-low latency execution pipelines and the computational graph/storage infrastructure that powers real-time pricing, risk, and quant research.

What you will do :

• Turn incomplete requirements into a clear problem statement. Understand who needs the solution, which constraints matter and how success will be measured.
• Investigate difficult questions through reading, calculations, profiling and experiments. Separate observations from assumptions and find the causes that affect the decision.
• Compare plausible approaches, explain their costs and limitations, and make a recommendation. Challenge the proposed direction when the evidence supports a better one.
• Architect stateful, event-driven computational graphs where analytical models process high-frequency event streams and emit derived signals asynchronously with minimal thread contention.
• Design and build the experimental and persistent data infrastructure using columnar and time-series storage optimised for selective column reads, high compression, and fast interactive queries across billions of historical ticks.
• Architect sandboxed, isolated execution runtimes (container- or process-level) that allow quantitative researchers to dynamically backtest and deploy custom trading logic while isolating the core market gateways from jitter and contention.
• Build and operate solutions across their lifecycle, including testing, deployment, security, data correctness and recovery from failure.
• Design modular, clean domain boundaries and adapter interfaces that allow the platform to be decomposed and tailored for multi-client deployments without compromising latency on the execution path.
• Create code, interfaces and tools that other engineers can understand and extend. Use engineering review to improve decisions and share what you learn.

How responsibility works:

You will have substantial autonomy and direct accountability for your decisions. We expect you to recognise when a decision needs more evidence or input from others, make uncertainty visible and adjust the plan when new information changes it.

What we are looking for:

• Strong programming ability and a practical understanding of algorithms, data structures, state, concurrency and system behaviour. You can explain the mechanisms behind a design and recognise the limits of your knowledge.
• First-principles understanding of system mechanics: memory layouts, cache locality, CPU thread affinity, avoiding allocation on hot paths, binary serialisation, and network I/O bottlenecks.
• Experience delivering substantial software and taking responsibility for its behaviour in use. You can discuss a concrete decision, its alternatives and what happened after delivery.
• Breadth developed through different problems, systems or domains. You can transfer useful ideas between them and explain where an analogy stops being valid.
• A habit of investigating fundamentals. You have examples of pursuing a question beyond the first answer because understanding it changed the solution.
• Clear communication with engineers and people outside engineering. You can make a recommendation understandable without hiding its uncertainty or tradeoffs.
• Evidence of these abilities matters more than a particular job title or number of years. Experience in quantitative work, financial markets, demanding performance problems or complex data systems can be useful, but deep financial domain knowledge is not required. Alongside the abilities above, you should be able to reason about deterministic performance under extreme load.

Working with AI:

The team actively uses AI in research and software development. You should be able to direct these tools, investigate unfamiliar technologies and inspect the resulting code or reasoning. Responsibility for understanding the solution, checking important assumptions and verifying the outcome stays with the engineer.

What to share with us:

• Bring examples of work you helped deliver, a difficult problem you investigated and a decision you changed after learning something new. Explain your own contribution and the evidence behind your conclusions. Respect any confidentiality restrictions on previous work.
• Our technical discussion uses a fictional case and a small practical exercise. AI and reference materials are allowed. We assess your reasoning, investigation and ability to verify a result. Familiarity with a particular streaming framework is not required.

Location and work pattern:

• Full time. Flexible hybrid working, with on-site presence as needed.
• Key hubs are London, Cambridge, New York.