Simulation Engineer

Time Seventeen; Hong Kong; On-site; HKD 900,000 - 1,100,000; Full time

Job summary

Time Seventeen in Hong Kong is seeking a software engineer to build scalable simulation runtimes and scenario pipelines for geopolitical risk and catastrophe modeling. You will work with research, engineering, markets, and risk teams to ensure repeatable experiments, transparent inputs, and dependable production systems.

Candidates should have strong Python skills and experience turning experimental work into robust software.

Qualifications

• Strong software engineering skills in Python and production systems.
• Experience with simulation, probabilistic modeling, or ML.
• Ability to reason about complex system behavior and evaluation.

Responsibilities

• Develop scalable simulation runtimes, agent behaviors, and scenario pipelines.
• Integrate models and datasets while keeping experiments reproducible and observable.
• Measure fidelity, stability, latency, and cost across simulation workloads.
• Collaborate with researchers to move methods into production.
• Document decisions, assumptions, and limitations for wider reuse.

Skills

Python

Production Systems

Simulation

Machine Learning

Distributed Computing

Clear Communication

Job description

About Time Seventeen

We give risk a price. We create state prices for tail events.

About Time Seventeen

We give risk a price. We create state prices for tail events.

The world contains a large class of risks that determine where capital flows yet have no price today. Global supply chains are being reconfigured, while the geopolitical discount rates that inform consequential decisions remain difficult to quantify. Time Seventeen is building tradable synthetic indices and term structures for these risks.

Time Seventeen is infrastructure for geopolitical risk and catastrophe risk. We give risk a price. We create state prices for tail events. Our ambition is to turn fragmented judgments about individual events into continuous risk prices, then bring those prices into institutional valuation, trading, and risk-transfer workflows.

Our work connects event definitions, probabilistic forecasting, market data, index methodology, and execution infrastructure. We explore how prediction markets, actuarial models, insurance and reinsurance structures, and blockchain-based DeFi can contribute to a common language for uncertain outcomes. The challenge is to make risk legible across different horizons, exposures, and market participants, while keeping assumptions and limitations explicit.

Building this infrastructure requires more than a compelling forecast. Data must be traceable, events must have defensible resolution rules, methodologies must survive scrutiny, and products must account for liquidity, basis risk, operational resilience, and applicable legal constraints. We want institutions to be able to understand a risk price, challenge its construction, and assess how it relates to the exposure they actually hold.

We are building a focused team across research, engineering, markets, and risk. We value people who can move between deep specialist work and the practical decisions needed to build a company: identifying the important question, testing an approach, communicating the evidence, and carrying the result into a usable system.

About The Role

Build simulation systems that model participants, environments, and decisions with the reliability needed for real-world use.

You will build systems for exploring how risks evolve under different assumptions and scenarios. Researchers need repeatable experiments; product teams need interpretable outputs. You will connect those needs through reliable scenario pipelines that expose model inputs, uncertainty, and failure conditions.

You will work closely with colleagues across research, engineering, markets, and risk. We expect you to clarify assumptions early, share intermediate evidence, and make decisions that account for the users and systems downstream of your work. The scope can evolve as we learn, but each initiative should have a clear owner and a concrete result.

What you'll do

• Develop scalable simulation runtimes, agent behaviors, and scenario pipelines.
• Integrate models and datasets while keeping experiments reproducible and observable.
• Measure fidelity, stability, latency, and cost across simulation workloads.
• Collaborate with researchers to move promising methods into production.
• Build reproducible scenario runs with versioned inputs, explicit random seeds, sensitivity checks, and diagnostics for unstable or implausible outcomes.
• Document important decisions, assumptions, and limitations so that colleagues can review the work and extend it without relying on undocumented context.

What we're looking for

• Strong software engineering skills in Python and experience with production systems.
• Knowledge of simulation, machine learning, distributed computing, or probabilistic modeling.
• The ability to reason carefully about complex system behavior and evaluation.
• A track record of turning experimental work into dependable software.
• An ability to explain complex work clearly, distinguish evidence from assumptions, and update your view when new information warrants it.
• Interest in building infrastructure for geopolitical and catastrophe risk, with a willingness to learn the financial and technical context beyond your own specialty.

What success looks like

• Deliver a well-defined piece of work that advances a real research, product, or institutional workflow, with acceptance criteria agreed with the team.
• Make the quality of your work inspectable through appropriate analysis, tests, documentation, or operational evidence.
• Create reusable methods and clear handoffs that help the wider team work more effectively.

Location & working arrangement

This role is based in Hong Kong. Please include your location, availability, and any working-arrangement requirements in your application.

How we work

Time Seventeen is a focused, high-ownership team. We value clear thinking, direct communication, careful execution, and people who carry important work through to a result. We expect colleagues to surface inconvenient evidence, ask precise questions, and make the limits of their work explicit. Strong specialist judgment matters, as does the willingness to understand how your work affects research, markets, engineering, and risk. We prefer useful documentation and clear decisions to unnecessary process.