A common assumption about HFT is that it is all about shaving microseconds. That is only part of the job. In practice, the harder problems are often in system design, decision logic, and handling complexity across multiple exchanges.
At Wincent, we solve these problems mostly in Rust. That is only the technical part. The cultural part matters just as much. We expect engineers to understand why they are delivering a specific work, challenge status-quo, and ship work that creates value. We say things directly, keep politics out of the way, and do not wait for a long chain of approvals and make decisions.
The Technical Reality: Architecture Over Micro-Optimizations
The “god emperor” of engineering isn’t necessarily the person writing the fastest assembly code. It’s the engineer who can look at the entire system, identify the actual bottleneck, and solve the right problem. Sometimes that means redesigning part of the architecture. Sometimes it means improving how systems interact across exchanges. And sometimes it means shaving microseconds off a critical path because those microseconds directly impact profitability.
Because of this, our engineering spans a wide spectrum. Some engineers work close to the metal: networking, OS-level buffers, and memory layout. Others work closer to the trading logic, translating mathematical models from quantitative researchers into robust, production-ready systems.
The Expert System: Translating Math into Production
A large part of our engineering is building the decision logic that drives our trading systems. Quants and researchers continuously discover new patterns, signals, and sources of edge. Turning those ideas into production requires close collaboration between research and engineering, from mathematical models and data processing to the systems that deliver market data and execute orders in real time.
The engineering challenge is translating those mathematical models into a robust, production-ready system that reacts to real-time input data. This isn’t a one-way ticket-taking process. Engineers collaborate directly with researchers, pushing back on what is computationally feasible, and working together to find the optimal tradeoff between a theoretically perfect model and a fast, reliable implementation.
Why We Chose Rust
Because we started later than many established/traditional HFT firms, we had the opportunity to make different technology choices from day one. Rather than building on a large existing codebase, we could choose a modern toolchain that matched our needs.
Rust’s type system allows us to encode domain constraints directly into the type system, so the compiler catches mistakes for us. In trading, small representational mistakes are incredibly expensive. Selling 1 Bitcoin for $100,000 is very different from selling 100,000 Bitcoin for $1. By wrapping raw numbers into strict domain types (like Price and Amount), we prevent these catastrophic mix-ups at compile time.
Threading: When to use Async vs. OS Threads
Rust doesn’t force a specific concurrency model on you. For many applications, an async runtime like Tokio is fantastic. It provides great developer productivity and handles high-throughput I/O beautifully using work-stealing queues.
However, in the critical path of a trading system, work-stealing queues introduce a scheduling latency penalty. If you need a continuation to be scheduled immediately and not after some random task finishes, you simply can’t afford that unpredictability. For our most latency-sensitive components, we bypass async runtimes entirely. We manage OS threads manually and pin them to specific CPU cores to ensure absolute predictability.
How the Codebase is Actually Structured
We think of it in layers. The performance-critical parts of the system, things like networking, OS-level buffers, serialization, those are stable. They don’t change often. They can contain unsafe code, but it’s highly localized. You can have an unsafe block where you tell the compiler “here I’m king, I’ll check everything on my own.” But that code is encapsulated behind safe APIs.
On top of that, you have the layer where we iterate fast: adding new trading strategies, connecting to new exchanges, tuning decision logic. Rust’s safety guarantees mean that a quant researcher, someone who is great at their job but isn’t a systems programmer with five years of writing memory-safe code, can write production Rust that is performant and won’t do something catastrophic.
Fire-side Chat: Engineering Culture at Wincent
We continued our conversation with Pavol, shifting from systems architecture to the day-to-day culture of the engineering team.
Q: How would you describe the day-to-day engineering culture at Wincent?
Pavol: One thing that stood out to me compared to previous engineering roles is how closely engineers are connected to business outcomes. You are expected to really own your problems. Engineers here have a massive amount of leverage. If you do a good job, you directly improve the company’s profitability. The flip side is that you’re part of the decision-making process, and your choices matter. It’s a high-stakes environment, but if you love solving hard problems with smart people, it’s incredibly rewarding.
Q: People often talk about “radical candor.” How does communication actually work on the team?
Pavol: We have a very open, frank culture. You could say it has roots in Eastern European directness. We don’t beat around the bush. If a decision is bad, people will tell you it’s bad.
People here tend to say things as they are. It actually makes work much less stressful because you spend less time guessing what others think and more time discussing problems openly. If someone believes a decision is wrong, they’re expected to say so directly.
Q: How do you maintain the quality of the engineering team as you grow?
Pavol: We focus heavily on talent density. Finding great candidates should always be difficult. If you find it easy, you need to raise your standards. My ideal candidate is someone who can come in and do my job better than me. We want people who are excited by that level of rigor, because we are competing against the best engineering teams in the world.
Q: Do you need a background in finance or HFT to get hired here?
Pavol: Not at all. Less than half of our engineering team came from a trading background. I personally came from ad-tech.
We don’t filter for finance experience; we filter for problem solvers. If you spent the last five years working on systems where reliability, correctness, or performance really mattered, and you are passionate about diving deep into technical challenges, we want to talk to you. We can teach you the trading domain. What we can’t teach is engineering judgment and the drive to take absolute ownership of a system.
