Site reliability engineering in proprietary and high frequency trading funds operates under very different assumptions from most technology organisations. In typical environments, reliability is defined in practical terms. Systems are expected to remain available, respond within acceptable timeframes, and recover from failure. These expectations shape how SRE teams work, from incident response to monitoring and service-level objectives. In high-frequency and proprietary trading, those standards are only the starting point. The goal is not simply to keep systems running, but to improve them continuously and automate them as deeply as possible.

Here, reliability is not about whether a system is reachable or even whether it responds in milliseconds. It is about whether the system behaves exactly as expected, repeatedly, under extreme load and at timescales that most engineers rarely work with. In these environments, a microsecond is not an abstract unit of measurement. It is a regular timescale, and often the difference between success and failure.

A trading system can be fully operational, returning valid responses with no errors, and still be functionally unreliable. In electronic markets, reliability is inseparable from timing. A system that is correct but late is often worse than a system that is briefly unavailable. At this scale, latency variation becomes a form of failure, and predictability becomes the highest expression of reliability. This reality forces a fundamental rethink of what reliability means, how it should be measured, and how SRE practices must evolve when operating at the sharp edge of modern financial markets.

Origins of Reliability

Traditional SRE thinking was shaped by web services and large-scale platforms, where small delays are acceptable, failures are part of normal operations, and systems can recover through retries, fallbacks, or gradual degradation. The title of “Site Reliability Engineer” was coined by Google in 2002 as their systems began to grow too large and complex for traditional IT/Ops models, they needed a way to keep services reliable at massive scale, avoid purely manual operations work and make reliability an engineering problem, not just an operational one. So instead of having separate development and operations teams, Google created SRE.

While some engineers in HFT’s held SRE responsibilities from ~2010 the skillset didn’t become more commercially known and sought after until ~2018, now it’s a common requirement for funds which SRE’s teams separated between support, automation and systems reliability.

SRE in HFT Environments

In HFT, there are no retries. There is no room for failure. There is only the first opportunity, and the next one belongs to someone else.

Latency variation is one of the main risks. A system that is consistently slower than competitors may still be viable if its behavior is predictable whereas a system that is sometimes fast and sometimes slow is not, due to it’s unpredictability and lack of reliability. In this world, averages are misleading, and even common percentile metrics can hide the behavior that actually determines outcomes. Reliability engineers responsibilities will also vary from fund to fund, some focused on a variation of speed, automation and trade relevance. For example market data SRE’s at Jump Trading are more concerned on speed of trade over trade value, an engineering director commented on one strategy being making basic trades the fastest.

What matters is how the system behaves in the worst moments, under peak market stress, when volumes surge, spreads widen, and every participant is competing for the same fleeting advantage. These are the moments when infrastructure is most likely to drift away from its baseline, and when traditional monitoring provides the least useful insight. Many firms only discover these problems indirectly, through weaker execution quality or unexplained P&L drawdowns. By the time the signal reaches the trading desk, the damage is already done .

One of the defining challenges of reliability at microsecond scale is that failures are rarely obvious. They do not appear as outages or error storms. Instead, they show up as subtle changes in system behaviour that still fall within what most tools consider “healthy”. A kernel scheduler may introduce slightly more jitter on one core than another. A garbage collector (automatic memory management) may pause a few microseconds longer under certain conditions. A network interface may reorder packets just frequently enough to disrupt downstream processing. None of these events will page an on-call engineer. All of them can materially affect trading outcomes.

These are partial failures, transient failures, and statistical failures. They exist in distributions rather than events, and they challenge the binary way reliability is often measured. At microsecond scale, reliability is not just about stopping systems from breaking. It is about stopping them from slowly drifting off course. In trading environments, systems rarely fail in dramatic ways. Performance usually degrades gradually. A small software update, a hardware change, or even a shift in market conditions can subtly affect how fast and consistently a system operates.

Because of this, continuous, high-resolution monitoring is critical. Tools like Grafana, Prometheus, and Datadog are commonly used to track latency, throughput, packet loss, CPU usage, memory pressure, and order-flow metrics in real time. These dashboards allow engineers to spot small performance regressions early — before they compound into missed fills, increased slippage, or elevated risk exposure. In high-frequency and systematic trading, reliability isn’t just about preventing outages — it’s about maintaining stable, predictable performance under constantly changing conditions.

Each change may seem harmless on its own. But over time, these small shifts add up. The system may still be “working,” yet its behaviour becomes less predictable. By the time the impact appears in trading results, it is often too late to fix easily. That is why reliability at this level has to be proactive. Teams need to understand not just whether systems are running, but how they are running, and whether that behavior remains consistent over time. This moves reliability away from reacting to failures and towards maintaining stable behavior under pressure.

Reliability teams in prop / HFT are typically split into 3 functions – support, systems and automation. Funds vary in preference / SRE approach across these teams. Hudson River Trading prefer hiring systems SRE’s who are competent coders but bring deep expertise in linux and networking whereas Citadel have traditionally hired your typical Google SRE profile who spend 80% of their time on deep application programming work. Funds are ever seeking procedural enhancements and a recent change in process has led Citadel Securities Systematic SRE team to transition back to operational support work and move away from their normal goal of deep automation and fixing, these more technical issues will go back to being handled by development / engineering teams, very similar to how processes used to run pre SRE. This approach can aid in more seamless and consistent day to day operational support, allowing larger engineering work to be correctly tracked in development teams.

Timing is not just about speed. It is about consistency. Market data must arrive in the right order. Decisions must be made at the right moment. Execution must happen within tight time windows, regardless of what else is happening in the market. To achieve this, firms must pay close attention to how their systems interact with the underlying technology they run on. How computing resources are used, how data moves through the system, and how different processes are scheduled all have a direct impact on performance.

A system can still appear healthy while quietly becoming less reliable. A small delay here, a brief interruption there, and suddenly the system is no longer behaving as expected. These changes are often too subtle to trigger alarms, but they can still affect trading outcomes. The same challenge applies to monitoring. To keep systems reliable, they must be measured. But measuring performance can itself introduce small delays. In trading, even tiny amounts of overhead matter.

Many traditional monitoring tools were built for slower environments where small delays are acceptable. In high-speed trading, they are not. As a result, firms often need lighter-weight ways to observe performance without interfering with it. Reliability at this level depends not just on what is measured, but how it is measured, when it is measured, and how much impact that measurement has.

Data quality is another critical factor. Trading systems receive vast amounts of market data from multiple sources. Reliability is not just about avoiding data loss. It is about making sure information arrives on time, in the correct order, and stays aligned with the system’s decisions.

A data feed that falls slightly behind may not raise any obvious warnings, but it can still cause trades to be made using outdated information. These issues often only become visible during periods of extreme market activity, when systems are under the most pressure. True reliability in this environment means understanding the full picture: how data flows in, how decisions are made, and how timing affects every step along the way. A feed handler that lags behind its peers may not trigger alerts, yet the resulting skew can lead to trades being priced on stale information. These failures are particularly difficult to detect because the system continues to function, but its decisions are subtly compromised.

In many cases, these problems only surface during periods of extreme market activity, when volumes spike and infrastructure is pushed beyond its normal limits. Ironically, these are also the moments when observability is most constrained and troubleshooting is most difficult. True reliability engineering in this context requires the ability to think about the system as a whole, across data ingestion, processing, and execution, with timing as the common thread.

Operational change is often where reliability efforts succeed or fail. In fast-moving markets, firms cannot freeze their infrastructure indefinitely. Hardware ages, software evolves, and competitive pressure demands continuous improvement. Yet every change introduces the risk of new sources of latency variation or unpredictable behaviour.

Traditional change management offers limited protection. Test environments rarely reflect real market complexity. Synthetic load tests struggle to capture the bursty, competitive nature of live trading. As a result, changes that appear safe in testing can behave very differently once deployed.This creates tension between innovation and stability. Some organisations become overly cautious, delaying upgrades and building technical debt. Others move quickly and accept higher risk, hoping to catch regressions before they cause serious damage. Neither approach is ideal. What is needed is a way to evaluate change through the lens of microsecond-level reliability. This means benchmarking not just correctness and throughput, but timing behaviour. It means comparing latency distributions, jitter patterns, and execution paths before and after meaningful changes. Change, in this world, must be measured as much as it is managed.

SRE system benchmarking

Benchmarking therefore becomes a core part of reliability engineering rather than an occasional exercise. The goal is not to produce impressive numbers, but to establish behavioural baselines that future system states can be compared against. Effective benchmarking focuses on repeatability and realism. Systems are tested under conditions that resemble real market behaviour, including bursts, contention, and correlated load. Measurements are taken at resolutions fine enough to reveal subtle shifts in performance, not just headline improvements.

Crucially, benchmarking must be continuous. In environments where performance degrades through drift rather than sudden failure, one-off tests create false confidence. Reliability is something that must be actively defended, day after day. When done well, benchmarking becomes an early warning system. It allows teams to detect deviations before they affect trading results, while the cost of correction is still low.

There isn’t one single metric for benchmarking, it’s more about defining a performance fingerprint not chasing a single number. Metrics will be split between:

• Latency (most important), order to exchange, data to decision, decision to fill.
• Jitter (latency consistency), two systems can have the same average latency but one is stable and one fluctuates, jitter measures variance in latency over time.
• Throughput (system load), orders per second, market data messages per second, risk checks per second. Used to detect saturation, backpressure, hidden bottlenecks.
• Error rates (small increases indicate deeper performance drift), rejected orders, dropped messages, timeouts, partial fills due to system delay.
• Resource efficiency (tracked alongside performance), CPU per order, memory per strategy, network bandwidth usage, cahce miss rates.

All of this has major implications for the role of SRE in high-frequency and proprietary trading firms. The traditional model of SRE as a support function focused on uptime is not sufficient. At microsecond scale, SRE must be closely connected to the business, with a clear understanding of how infrastructure behaviour affects trading performance.

SRE talent benchmarking

This requires a different skill set and mindset. Engineers must be comfortable working close to the hardware, reasoning about timing at fine detail, and collaborating directly with traders and quants. They must be able to translate reliability concerns into business impact, and business goals into technical priorities.

Culturally, this also demands a shift. Reliability cannot be treated as a defensive cost. It must be recognised as a source of competitive advantage. Firms that maintain tighter latency distributions, more predictable behaviour, and greater confidence in their systems gain not just stability, but strategic flexibility.

They can trade more aggressively, adapt more quickly, and take risks that others cannot. This statement is validated by Hudson River Trading’s success and approach to Site Reliability Engineering (although these don’t correlate directly). Through recent years HRT has rapidly become a leading contender, only competing on latency with the likes of Jane Street and Citadel Securities. for pound HRT have one of the largest appetites for SRE hiring which spans Trade Operations, Research & Development and Systems / Infrastructure. SRE’s within the TradeOps team, they’ve recently increased efforts even further to onboard more competent SRE’s globally within each team. Within the “TradeOps” team you have engineers (roughly 20 globally) who are both competent quant developers and traders, so along with development and maintenance of trading systems, they also contribute towards the placement of trades, this is an unusual but successful approach and shows the depth SRE’s can work to.

As markets continue to compress time and competition intensifies, the margin for error will shrink further. Microseconds that once seemed irrelevant now separate leaders from laggards. In this environment, reliability engineering is no longer about keeping systems running. It is about ensuring systems behave exactly as intended, even under extreme conditions. Reliability at microsecond scale is not simply an extension of traditional SRE. It is a distinct discipline that blends systems engineering, performance analysis, and market awareness into a single practice. For firms willing to embrace this reality, reliability becomes more than an operational requirement. It becomes the foundation of modern electronic trading.

And in that world, SRE is not just about protecting the business. It is about enabling it to win.

Just as trading systems are benchmarked, the engineers who operate them are measured with equal standards. High-frequency and proprietary trading firms rarely hire based on traditional metrics alone. Resumes and titles matter far less than the ability to think in terms of time, determinism, and the subtle interplay between system behavior and market outcomes. The question is never simply “Can this candidate write code?” It is, “Can they reason about a system’s behavior under pressure, predict how it will respond, and act before a tiny drift turns into a tangible loss?”

Assessing talent at this level is less about standard interviews and more about behavioral baselines under realistic conditions. Candidates are challenged not only to solve problems but to do so consistently, reliably, and without introducing variance. They must demonstrate an instinct for subtle failures — the microsecond jitters, the tiny shifts in packet ordering, the barely perceptible pauses that could silently erode trading performance. Correctness alone is not enough; a correct solution that wavers under load or loses precision at scale is fundamentally unreliable.

While some of the best engineers come from other HFT and prop trading firms, the supply rarely meets the demand. The most effective teams look beyond the obvious, searching for engineers whose experience in other high-performance, timing-sensitive domains translates directly to microsecond reliability. Market data vendors and exchange technology providers (such as Bloomberg, Refinitiv, Nasdaq, CME Group) often cultivate engineers accustomed to handling vast streams of data with precision and minimal delay. Firms building ultra-low-latency networking hardware (like Arista Networks, Cisco’s high-performance trading division, or Juniper Networks) develop specialists who understand how to move information across systems with extreme consistency.

Fields like real-time robotics and autonomous systems (including companies such as Boston Dynamics, Waymo, Tesla Autopilot, or ABB Robotics) produce engineers who think about timing, control, and system behaviour under physical constraints, where even small delays can have real-world consequences. Industrial automation and high-speed manufacturing (Siemens, Rockwell Automation, Fanuc) also demand deterministic system behaviour, making their engineers well suited to environments where predictability matters more than raw speed alone.

Even operating system and kernel development, traditionally far from the trading floor, teaches lessons that translate directly to HFT infrastructure. Engineers working on Linux, real-time operating systems, or low-level performance tooling (Red Hat, Canonical, Intel, ARM) develop a deep understanding of scheduling, memory, and latency behaviour — the hidden mechanics that often decide whether a trading system performs reliably at microsecond scale.

Recruitment at this scale becomes an exercise in continuous measurement. Firms do not simply hire once and hope for the best; they observe candidates over multiple interactions, across coding exercises, live problem-solving sessions, and simulated bursts of load. The goal is to see not just whether someone succeeds, but how reliably they succeed under pressure. In high-frequency trading, just as in the systems themselves, it is the consistency and predictability of performance that separates those who can operate at the sharp edge from those who cannot.

In a sense, benchmarking talent becomes a natural extension of benchmarking infrastructure. Engineers are expected to maintain the same standards of reliability that the systems themselves are held to. Drift, inconsistency, and unpredictability are as dangerous in human performance as they are in machines. Firms that understand this align recruitment, training, and ongoing evaluation with the same principles that guide their SRE practices: a relentless focus on repeatability, subtle failure detection, and the proactive maintenance of performance.

By approaching talent with the same rigor as infrastructure, trading firms ensure that their systems are not only fast, but also trusted, predictable, and resilient — and that the people behind those systems are capable of sustaining that advantage when the pressure is highest. In microsecond-scale environments, true reliability is a combination of hardware, software, and human judgment, and the margin for error is measured not in hours or minutes, but in the tiniest fractions of a second.