AI Engineering Intern

InternHunt; San Francisco (CA); Hybrid; USD 120,000 - 170,000; Full time

Job summary

Time Seventeen is seeking a driven engineer to build and evaluate AI workflows that turn research ideas into practical tools for event analysis and risk infrastructure. You will push an AI prototype along an engineering path to a dependable workflow, including document ingestion, retrieval, and structured extraction.

Collaboration with research and product teams will refine a bounded project with clear acceptance criteria.

Qualifications

• Proven Python or TypeScript development experience.
• Familiarity with APIs, databases, and version control practices.
• Experience experimenting with language models or AI tooling.
• Interest in reliable systems and evidence-based evaluation.
• Showcase a project or code sample demonstrating learning and contribution.

Responsibilities

• Build small services and workflows around model APIs and retrieval systems.
• Implement input validation, output schemas, retries and logging.
• Create evaluation scenarios for factual support and extraction accuracy.
• Measure latency and cost; investigate bottlenecks without compromising quality.
• Connect prototypes to usable APIs or interfaces and document dependencies.
• Review code with engineers and turn experiments into reusable components.

Skills

Python

TypeScript

APIs

Databases

Version control

Software testing

Language models

Retrieval

Evidence-based evaluation

Job description

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 and evaluate AI workflows that turn research ideas into practical tools for event analysis and risk infrastructure.

You will work on the engineering path from an AI prototype to a dependable workflow. That can include document ingestion, retrieval, structured extraction, tool integration, and interfaces that expose the evidence behind an output. You will learn to make behavior measurable and failures visible.

You will collaborate with research and product colleagues on a bounded project with a clear user and acceptance criteria. The work will involve iteration across functionality, latency, cost, reliability, and output quality, with careful attention to how the system behaves when sources or models change.

What you'll do

• Build small services and workflows around model APIs, retrieval systems, and structured event data.
• Implement input validation, output schemas, retries, error handling, and useful logging.
• Create evaluation cases for factual support, extraction accuracy, and workflow completion.
• Measure latency and cost and investigate bottlenecks without obscuring quality tradeoffs.
• Connect prototypes to usable APIs or interfaces and document their dependencies.
• Review code with engineering colleagues and help turn validated experiments into reusable components.

What we're looking for

• Practical Python or TypeScript experience and an ability to build and debug a working application.
• Familiarity with APIs, databases, version control, and basic software testing.
• Experience experimenting with language models, retrieval, or other AI tooling.
• An interest in reliable systems and evidence-based evaluation.
• A project or code sample that demonstrates your contribution and ability to learn.

What success looks like

• Ship a working AI workflow with clear inputs, outputs, and failure handling.
• Make quality, latency, and cost inspectable through an evaluation and instrumentation.
• Leave code and documentation that colleagues can run and extend.

Location & working arrangement

This role is associated with San Francisco and supports remote work. Please share your location, time zone, availability, and ability to coordinate with colleagues in San Francisco and Hong Kong.

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.