Quantitative Developer Intern, 2027

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 research data pipelines, backtesting tools, and reliable quantitative software in Hong Kong beginning May 2027. You will work with researchers and engineers to turn quantitative ideas into reliable, reusable software. The focus is the infrastructure behind research: consistent data definitions, reproducible experiments, tested numerical code, and tools that colleagues can run and maintain. Summer projects may include a market-data ingestion pipeline, a research dataset interface, a backtesting component, or a profiling and validation tool. You will own a scoped engineering project with code review, documented acceptance criteria, and a practical handoff.

What you'll do
• Build and test data pipelines for prediction-market prices, order books, event metadata, and outcomes.
• Develop reusable research libraries and backtesting components with explicit timestamp, unit, and numerical assumptions.
• Implement checks for duplicate records, missing data, schema changes, and reproducibility across runs.
• Profile data-processing and simulation workloads; measure throughput, memory use, and numerical correctness before and after changes.
• Collaborate with researchers to translate a research specification into reviewed, maintainable code.
• Write automated tests, setup instructions, and clear documentation for delivered tools.
• Build data interfaces with explicit schema, units, event identifiers, time zones, and versioned transformations; reconcile raw inputs with derived research outputs.
• Test malformed inputs, duplicate messages, missing partitions, interrupted jobs, and numerical edge cases; document safe retries and recovery behavior.
• Benchmark against a reproducible baseline and report dataset sizes, hardware assumptions, correctness checks, runtime, and memory use.
• Participate in code reviews, break work into inspectable milestones, and leave examples and a handoff that another researcher can run. What we're looking for
• Currently studying, or recently graduated in, mathematics, statistics, computer science, physics, engineering, econometrics, quantitative finance, or a related discipline. Evidence from rigorous independent study and projects is also welcome.
• A strong foundation in probability, conditional expectation, distributions, estimation, hypothesis testing, regression, and the interpretation of uncertainty; explain the assumptions and limitations of the methods you use.
• Comfort with linear algebra, calculus, numerical methods, and basic optimization, including checking units, numerical stability, and whether a result is economically and statistically plausible.
• Practical Python proficiency, including NumPy and pandas, and the ability to write readable, modular code for data processing, analysis, and experiments. Submit a project that demonstrates your own implementation and debugging work.
• Working knowledge of data structures and algorithms, Git, and automated testing. Be able to document dependencies, reproduce an experiment from a clean environment, and explain design and complexity tradeoffs.
• Ability to query and validate structured datasets, align timestamps and event identifiers, handle missing or duplicate observations, and document source provenance. SQL and Linux experience are helpful.
• Understanding of overfitting, chronological train/test splits, look-ahead bias, leakage, selection bias, and repeated experimentation. Evaluate a proposed method against simple baselines and meaningful out-of-sample tests.
• Interest in prediction markets and market microstructure: event contracts, resolution rules, probabilities, order books, spreads, liquidity, fees, and settlement. Prior professional trading experience is not required.
• Ability to distinguish quoted prices from executable prices, account for transaction costs and liquidity, and state where a backtest or simulator relies on assumptions that may not hold in practice.
• Evidence of a substantive quantitative or software project, from coursework, independent research, an internship, or open source. Explain the question, your personal contribution, validation, limitations, and what you would improve.
• Clear written and spoken English, careful research notes, and the ability to communicate technical findings and uncertainty to colleagues across research and engineering. Incorporate review feedback and report negative results honestly.
• Available to work from Hong Kong during the agreed internship period. Share your current location, academic commitments, weekly availability, and relevant work-authorization or sponsorship requirements; eligibility will be discussed individually.
• For quantitative development, demonstrate software design, systematic debugging, and testing of data or numerical code. Understand interfaces, schema validation, reproducibility, and recovery from common pipeline failures.
• Experience with Rust, C++, another systems language, asynchronous I/O, profiling, or larger datasets is helpful. Be able to measure throughput and memory use while preserving numerical and data correctness. What success looks like
• Deliver a reviewed research tool or pipeline that colleagues can run from documented instructions.
• Demonstrate data correctness, reproducible outputs, and recovery from relevant failure cases.
• Leave maintainable code, meaningful tests, and a clear account of performance and design tradeoffs. Location & working arrangement This internship is based in Hong Kong and begins in May 2027. Please share your exact available start and end dates, weekly availability, current location, academic commitments, and work-authorization or sponsorship requirements. Any scheduling constraints will be discussed during the interview process.

How to apply
Share your CV and a software, data-engineering, or quantitative tooling project. Explain your implementation, architecture, personal contribution, tests, failure cases, performance measurements, and design tradeoffs. Coursework and independent projects are welcome. Confirm your availability in Hong Kong from May 2027, with exact dates and weekly commitments. Exclude confidential employer, client, or research information. 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. Apply for this role→