QT Pilot is a quantitative trading firm actively deploying real capital across US equity and futures markets. We are seeking a Senior Quantitative Developer to take end-to-end ownership of our trading systems: broker connectivity, the execution engine, the strategy platform, system observability, and the cloud infrastructure that ensures seamless operations throughout the US trading session.
In this role, you will collaborate closely with the Quantitative Research Team Lead in Warsaw and the Data Engineering team in Bangkok. Your responsibilities will span both low-level infrastructure (broker gateways, execution engines, and cloud architecture) and the higher-level platform on which our Quantitative Researchers build and deploy their strategies.
Location: Warsaw, Poland Employment Type: Permanent, Hybrid (4 days from the office)
Responsibilities
Broker connectivity and execution
Own the broker/exchange integration layer: Take complete responsibility for everything between our trading framework and the brokers/exchanges. This includes managing gateway lifecycles, sessions and client IDs, reconnection logic, market-data entitlements, and maintaining the custom patch layer we currently run on top of the Interactive Brokers (IB) adapter.
Robust multi-leg execution systems: Ensure our execution engine can reliably handle complex edge cases, such as partial fills, cancel-fill race conditions, duplicate or missing execution reports, and accurate fill attribution when multiple deployments share a single account.
Implement strict reconciliation mechanisms: Treat the broker as the absolute source of truth. You will build systems to reconcile positions, open orders, and fills at every startup and at scheduled intervals during the trading session, ensuring that any detected divergence automatically halts the trading fleet to mitigate risk.
Architect for scalability: Lay the technical groundwork for integrating a second venue or a prime broker seamlessly, ensuring that new integrations require zero modifications to existing strategy code.
Reliability and operations
Take ownership of production: Act as the primary production owner responsible for system uptime throughout the US trading session. Manage deployments, restarts, rollbacks, and emergency halt procedures, which includes being on-call during active market hours.
Ensure fast, uneventful cold starts: Architect systems for deterministic recovery by maintaining a bounded state in the event cache. Guarantee that recovery procedures never rely on a warm cache to resume operations after an incident.
Summary
QT Pilot is a quantitative trading firm actively deploying real capital across US equity and futures markets. We are seeking a Senior Quantitative Developer to take end-to-end ownership of our trading systems: broker connectivity, the execution engine, the strategy platform, system observability, and the cloud infrastructure that ensures seamless operations throughout the US trading session.
In this role, you will collaborate closely with the Quantitative Research Team Lead in Warsaw and the Data Engineering team in Bangkok. Your responsibilities will span both low-level infrastructure (broker gateways, execution engines, and cloud architecture) and the higher-level platform on which our Quantitative Researchers build and deploy their strategies.
Location: Warsaw, Poland Employment Type: Permanent, Hybrid (4 days from the office)
Responsibilities
Broker connectivity and execution
Own the broker/exchange integration layer: Take complete responsibility for everything between our trading framework and the brokers/exchanges. This includes managing gateway lifecycles, sessions and client IDs, reconnection logic, market-data entitlements, and maintaining the custom patch layer we currently run on top of the Interactive Brokers (IB) adapter.
Robust multi-leg execution systems: Ensure our execution engine can reliably handle complex edge cases, such as partial fills, cancel-fill race conditions, duplicate or missing execution reports, and accurate fill attribution when multiple deployments share a single account.
Implement strict reconciliation mechanisms: Treat the broker as the absolute source of truth. You will build systems to reconcile positions, open orders, and fills at every startup and at scheduled intervals during the trading session, ensuring that any detected divergence automatically halts the trading fleet to mitigate risk.
Architect for scalability: Lay the technical groundwork for integrating a second venue or a prime broker seamlessly, ensuring that new integrations require zero modifications to existing strategy code.
Reliability and operations
Take ownership of production: Act as the primary production owner responsible for system uptime throughout the US trading session. Manage deployments, restarts, rollbacks, and emergency halt procedures, which includes being on-call during active market hours.
Ensure fast, uneventful cold starts: Architect systems for deterministic recovery by maintaining a bounded state in the event cache. Guarantee that recovery procedures never rely on a warm cache to resume operations after an incident.
QT Pilot is a quantitative trading firm actively deploying real capital across US equity and futures markets. We are seeking a Senior Quantitative Developer to take end-to-end ownership of our trading systems: broker connectivity, the execution engine, the strategy platform, system observability, and the cloud infrastructure that ensures seamless operations throughout the US trading session.
In this role, you will collaborate closely with the Quantitative Research Team Lead in Warsaw and the Data Engineering team in Bangkok. Your responsibilities will span both low-level infrastructure (broker gateways, execution engines, and cloud architecture) and the higher-level platform on which our Quantitative Researchers build and deploy their strategies.
Location: Warsaw, Poland Employment Type: Permanent, Hybrid (4 days from the office)
Responsibilities
Broker connectivity and execution
Own the broker/exchange integration layer: Take complete responsibility for everything between our trading framework and the brokers/exchanges. This includes managing gateway lifecycles, sessions and client IDs, reconnection logic, market-data entitlements, and maintaining the custom patch layer we currently run on top of the Interactive Brokers (IB) adapter.
Robust multi-leg execution systems: Ensure our execution engine can reliably handle complex edge cases, such as partial fills, cancel-fill race conditions, duplicate or missing execution reports, and accurate fill attribution when multiple deployments share a single account.
Implement strict reconciliation mechanisms: Treat the broker as the absolute source of truth. You will build systems to reconcile positions, open orders, and fills at every startup and at scheduled intervals during the trading session, ensuring that any detected divergence automatically halts the trading fleet to mitigate risk.
Architect for scalability: Lay the technical groundwork for integrating a second venue or a prime broker seamlessly, ensuring that new integrations require zero modifications to existing strategy code.
Reliability and operations
Take ownership of production: Act as the primary production owner responsible for system uptime throughout the US trading session. Manage deployments, restarts, rollbacks, and emergency halt procedures, which includes being on-call during active market hours.
Ensure fast, uneventful cold starts: Architect systems for deterministic recovery by maintaining a bounded state in the event cache. Guarantee that recovery procedures never rely on a warm cache to resume operations after an incident.
Summary
QT Pilot is a quantitative trading firm actively deploying real capital across US equity and futures markets. We are seeking a Senior Quantitative Developer to take end-to-end ownership of our trading systems: broker connectivity, the execution engine, the strategy platform, system observability, and the cloud infrastructure that ensures seamless operations throughout the US trading session.
In this role, you will collaborate closely with the Quantitative Research Team Lead in Warsaw and the Data Engineering team in Bangkok. Your responsibilities will span both low-level infrastructure (broker gateways, execution engines, and cloud architecture) and the higher-level platform on which our Quantitative Researchers build and deploy their strategies.
Location: Warsaw, Poland Employment Type: Permanent, Hybrid (4 days from the office)
Responsibilities
Broker connectivity and execution
Own the broker/exchange integration layer: Take complete responsibility for everything between our trading framework and the brokers/exchanges. This includes managing gateway lifecycles, sessions and client IDs, reconnection logic, market-data entitlements, and maintaining the custom patch layer we currently run on top of the Interactive Brokers (IB) adapter.
Robust multi-leg execution systems: Ensure our execution engine can reliably handle complex edge cases, such as partial fills, cancel-fill race conditions, duplicate or missing execution reports, and accurate fill attribution when multiple deployments share a single account.
Implement strict reconciliation mechanisms: Treat the broker as the absolute source of truth. You will build systems to reconcile positions, open orders, and fills at every startup and at scheduled intervals during the trading session, ensuring that any detected divergence automatically halts the trading fleet to mitigate risk.
Architect for scalability: Lay the technical groundwork for integrating a second venue or a prime broker seamlessly, ensuring that new integrations require zero modifications to existing strategy code.
Reliability and operations
Take ownership of production: Act as the primary production owner responsible for system uptime throughout the US trading session. Manage deployments, restarts, rollbacks, and emergency halt procedures, which includes being on-call during active market hours.
Ensure fast, uneventful cold starts: Architect systems for deterministic recovery by maintaining a bounded state in the event cache. Guarantee that recovery procedures never rely on a warm cache to resume operations after an incident.
Drive incident resolution and reliability: Conduct thorough postmortems for every incident, ensuring that each review yields a concrete new test or monitor. Keep operational runbooks consistently up to date through active, daily use.
Manage and secure cloud infrastructure: Maintain pristine cloud environments by managing dedicated systemd units per deployment and centralizing server access. Securely handle secrets (keeping live environment data strictly out of version control), manage reliable backups of persisted state, and optimize resources to ensure predictable cloud costs.
Performance
Optimize system latency: Rigorously measure decision-to-order and broker-event-to-strategy latency, and engineer robust solutions to actively minimize both.
Scale and profile performance: Profile deployments under live market data to confidently scale operations—such as expanding from 5 to 20 baskets while ensuring concurrent processes do not suffer from resource starvation or contention.
Strategy platform and API
Own the strategy interface: Manage the API and interfaces that our quantitative strategies are written against. Ensure researchers can easily define and utilize spread construction, hedge ratios, target inventory, risk caps, and order intent without ever needing to interact with the underlying broker code.
Develop the strategy toolkit: Build a comprehensive ecosystem to support strategy lifecycle and deployment. This includes enforcing configuration schemas and validation, tracking parameter provenance, enabling replayable market recordings, building a local simulator utilizing recorded data, and enabling one-click paper trading deployments.
Secure the research-to-production pipeline: Strictly enforce the boundary between research environments and live trading. Ensure that strategy code is written once, properly versioned and registered, and promoted to production exclusively through a tightly controlled, standardized import workflow.
Minimize technical debt: Actively push framework fixes upstream to core libraries, ensuring that our local patch layer continuously shrinks over time rather than accumulating.
Observability and controls
Develop comprehensive observability dashboards: Build monitoring views that allow us to determine the health of the entire trading fleet in under a minute. Track critical metrics including system heartbeats, positions versus targets, gross notional exposure versus caps, fill latency, data staleness, and order reject rates.
Codify pre-trade risk management: Ensure all pre-trade risk controls live strictly in code. Implement notional gates, per-basket caps, and automated kill switches, supported by rigorous acceptance tests that prove every safeguard fires exactly as intended.
Automate daily reconciliation: Guarantee that internal Profit and Loss (PnL) and market exposure accurately reconcile against official broker statements every single day.
Required Qualifications (Must have)
3-5+ years of Python in production where a bug cost money. Comfortable with asyncio, concurrency bugs, and reading framework source when the docs run out.
Writes Rust. Has shipped it or contributed to a Rust codebase of real size. Can take our Python adapter fixes and re-express them against a Rust adapter.
Has fought at least one broker or exchange API for real. IB TWS API or Gateway is a strong plus. FIX helps for the next venue.
Knows the order lifecycle cold. States, acks, partials, cancels, replaces, and the ways each venue bends the rules.
Runs Linux without hand-holding. System, journals, networking, one cloud provider, infrastructure in code or near it. Has had Redis or something like it fall over under load.
Tests the hard parts. Unit tests on adapter logic, replay tests over recorded sessions, acceptance tests on risk controls.
Writes incident reports and architecture decisions that other people can act on.
Preferred Qualifications (Strong plus)
Time on NautilusTrader, Lean, Hummingbot, or an in-house event-driven trading framework.
Enough pairs or basket stat-arb exposure to know why one filled leg is an emergency.
Has worked somewhere small enough that the person who built it also carried the pager.
Drive incident resolution and reliability: Conduct thorough postmortems for every incident, ensuring that each review yields a concrete new test or monitor. Keep operational runbooks consistently up to date through active, daily use.
Manage and secure cloud infrastructure: Maintain pristine cloud environments by managing dedicated systemd units per deployment and centralizing server access. Securely handle secrets (keeping live environment data strictly out of version control), manage reliable backups of persisted state, and optimize resources to ensure predictable cloud costs.
Performance
Optimize system latency: Rigorously measure decision-to-order and broker-event-to-strategy latency, and engineer robust solutions to actively minimize both.
Scale and profile performance: Profile deployments under live market data to confidently scale operations—such as expanding from 5 to 20 baskets while ensuring concurrent processes do not suffer from resource starvation or contention.
Strategy platform and API
Own the strategy interface: Manage the API and interfaces that our quantitative strategies are written against. Ensure researchers can easily define and utilize spread construction, hedge ratios, target inventory, risk caps, and order intent without ever needing to interact with the underlying broker code.
Develop the strategy toolkit: Build a comprehensive ecosystem to support strategy lifecycle and deployment. This includes enforcing configuration schemas and validation, tracking parameter provenance, enabling replayable market recordings, building a local simulator utilizing recorded data, and enabling one-click paper trading deployments.
Secure the research-to-production pipeline: Strictly enforce the boundary between research environments and live trading. Ensure that strategy code is written once, properly versioned and registered, and promoted to production exclusively through a tightly controlled, standardized import workflow.
Minimize technical debt: Actively push framework fixes upstream to core libraries, ensuring that our local patch layer continuously shrinks over time rather than accumulating.
Observability and controls
Develop comprehensive observability dashboards: Build monitoring views that allow us to determine the health of the entire trading fleet in under a minute. Track critical metrics including system heartbeats, positions versus targets, gross notional exposure versus caps, fill latency, data staleness, and order reject rates.
Codify pre-trade risk management: Ensure all pre-trade risk controls live strictly in code. Implement notional gates, per-basket caps, and automated kill switches, supported by rigorous acceptance tests that prove every safeguard fires exactly as intended.
Automate daily reconciliation: Guarantee that internal Profit and Loss (PnL) and market exposure accurately reconcile against official broker statements every single day.
Required Qualifications (Must have)
3-5+ years of Python in production where a bug cost money. Comfortable with asyncio, concurrency bugs, and reading framework source when the docs run out.
Writes Rust. Has shipped it or contributed to a Rust codebase of real size. Can take our Python adapter fixes and re-express them against a Rust adapter.
Has fought at least one broker or exchange API for real. IB TWS API or Gateway is a strong plus. FIX helps for the next venue.
Knows the order lifecycle cold. States, acks, partials, cancels, replaces, and the ways each venue bends the rules.
Runs Linux without hand-holding. System, journals, networking, one cloud provider, infrastructure in code or near it. Has had Redis or something like it fall over under load.
Tests the hard parts. Unit tests on adapter logic, replay tests over recorded sessions, acceptance tests on risk controls.
Writes incident reports and architecture decisions that other people can act on.
Preferred Qualifications (Strong plus)
Time on NautilusTrader, Lean, Hummingbot, or an in-house event-driven trading framework.
Enough pairs or basket stat-arb exposure to know why one filled leg is an emergency.
Has worked somewhere small enough that the person who built it also carried the pager.
Drive incident resolution and reliability: Conduct thorough postmortems for every incident, ensuring that each review yields a concrete new test or monitor. Keep operational runbooks consistently up to date through active, daily use.
Manage and secure cloud infrastructure: Maintain pristine cloud environments by managing dedicated systemd units per deployment and centralizing server access. Securely handle secrets (keeping live environment data strictly out of version control), manage reliable backups of persisted state, and optimize resources to ensure predictable cloud costs.
Performance
Optimize system latency: Rigorously measure decision-to-order and broker-event-to-strategy latency, and engineer robust solutions to actively minimize both.
Scale and profile performance: Profile deployments under live market data to confidently scale operations—such as expanding from 5 to 20 baskets while ensuring concurrent processes do not suffer from resource starvation or contention.
Strategy platform and API
Own the strategy interface: Manage the API and interfaces that our quantitative strategies are written against. Ensure researchers can easily define and utilize spread construction, hedge ratios, target inventory, risk caps, and order intent without ever needing to interact with the underlying broker code.
Develop the strategy toolkit: Build a comprehensive ecosystem to support strategy lifecycle and deployment. This includes enforcing configuration schemas and validation, tracking parameter provenance, enabling replayable market recordings, building a local simulator utilizing recorded data, and enabling one-click paper trading deployments.
Secure the research-to-production pipeline: Strictly enforce the boundary between research environments and live trading. Ensure that strategy code is written once, properly versioned and registered, and promoted to production exclusively through a tightly controlled, standardized import workflow.
Minimize technical debt: Actively push framework fixes upstream to core libraries, ensuring that our local patch layer continuously shrinks over time rather than accumulating.
Observability and controls
Develop comprehensive observability dashboards: Build monitoring views that allow us to determine the health of the entire trading fleet in under a minute. Track critical metrics including system heartbeats, positions versus targets, gross notional exposure versus caps, fill latency, data staleness, and order reject rates.
Codify pre-trade risk management: Ensure all pre-trade risk controls live strictly in code. Implement notional gates, per-basket caps, and automated kill switches, supported by rigorous acceptance tests that prove every safeguard fires exactly as intended.
Automate daily reconciliation: Guarantee that internal Profit and Loss (PnL) and market exposure accurately reconcile against official broker statements every single day.
Required Qualifications (Must have)
3-5+ years of Python in production where a bug cost money. Comfortable with asyncio, concurrency bugs, and reading framework source when the docs run out.
Writes Rust. Has shipped it or contributed to a Rust codebase of real size. Can take our Python adapter fixes and re-express them against a Rust adapter.
Has fought at least one broker or exchange API for real. IB TWS API or Gateway is a strong plus. FIX helps for the next venue.
Knows the order lifecycle cold. States, acks, partials, cancels, replaces, and the ways each venue bends the rules.
Runs Linux without hand-holding. System, journals, networking, one cloud provider, infrastructure in code or near it. Has had Redis or something like it fall over under load.
Tests the hard parts. Unit tests on adapter logic, replay tests over recorded sessions, acceptance tests on risk controls.
Writes incident reports and architecture decisions that other people can act on.
Preferred Qualifications (Strong plus)
Time on NautilusTrader, Lean, Hummingbot, or an in-house event-driven trading framework.
Enough pairs or basket stat-arb exposure to know why one filled leg is an emergency.
Has worked somewhere small enough that the person who built it also carried the pager.
Drive incident resolution and reliability: Conduct thorough postmortems for every incident, ensuring that each review yields a concrete new test or monitor. Keep operational runbooks consistently up to date through active, daily use.
Manage and secure cloud infrastructure: Maintain pristine cloud environments by managing dedicated systemd units per deployment and centralizing server access. Securely handle secrets (keeping live environment data strictly out of version control), manage reliable backups of persisted state, and optimize resources to ensure predictable cloud costs.
Performance
Optimize system latency: Rigorously measure decision-to-order and broker-event-to-strategy latency, and engineer robust solutions to actively minimize both.
Scale and profile performance: Profile deployments under live market data to confidently scale operations—such as expanding from 5 to 20 baskets while ensuring concurrent processes do not suffer from resource starvation or contention.
Strategy platform and API
Own the strategy interface: Manage the API and interfaces that our quantitative strategies are written against. Ensure researchers can easily define and utilize spread construction, hedge ratios, target inventory, risk caps, and order intent without ever needing to interact with the underlying broker code.
Develop the strategy toolkit: Build a comprehensive ecosystem to support strategy lifecycle and deployment. This includes enforcing configuration schemas and validation, tracking parameter provenance, enabling replayable market recordings, building a local simulator utilizing recorded data, and enabling one-click paper trading deployments.
Secure the research-to-production pipeline: Strictly enforce the boundary between research environments and live trading. Ensure that strategy code is written once, properly versioned and registered, and promoted to production exclusively through a tightly controlled, standardized import workflow.
Minimize technical debt: Actively push framework fixes upstream to core libraries, ensuring that our local patch layer continuously shrinks over time rather than accumulating.
Observability and controls
Develop comprehensive observability dashboards: Build monitoring views that allow us to determine the health of the entire trading fleet in under a minute. Track critical metrics including system heartbeats, positions versus targets, gross notional exposure versus caps, fill latency, data staleness, and order reject rates.
Codify pre-trade risk management: Ensure all pre-trade risk controls live strictly in code. Implement notional gates, per-basket caps, and automated kill switches, supported by rigorous acceptance tests that prove every safeguard fires exactly as intended.
Automate daily reconciliation: Guarantee that internal Profit and Loss (PnL) and market exposure accurately reconcile against official broker statements every single day.
Required Qualifications (Must have)
3-5+ years of Python in production where a bug cost money. Comfortable with asyncio, concurrency bugs, and reading framework source when the docs run out.
Writes Rust. Has shipped it or contributed to a Rust codebase of real size. Can take our Python adapter fixes and re-express them against a Rust adapter.
Has fought at least one broker or exchange API for real. IB TWS API or Gateway is a strong plus. FIX helps for the next venue.
Knows the order lifecycle cold. States, acks, partials, cancels, replaces, and the ways each venue bends the rules.
Runs Linux without hand-holding. System, journals, networking, one cloud provider, infrastructure in code or near it. Has had Redis or something like it fall over under load.
Tests the hard parts. Unit tests on adapter logic, replay tests over recorded sessions, acceptance tests on risk controls.
Writes incident reports and architecture decisions that other people can act on.
Preferred Qualifications (Strong plus)
Time on NautilusTrader, Lean, Hummingbot, or an in-house event-driven trading framework.
Enough pairs or basket stat-arb exposure to know why one filled leg is an emergency.
Has worked somewhere small enough that the person who built it also carried the pager.
