Trading Systems Engineer

About Fact Machine

Fact Machine is reinventing how people engage with opinions online. We run 24-hour prediction markets on subjective questions - think "Should the US acquire Greenland?" or "Is this the right take?"- where users trade on predicted consensus outcomes. We've raised $8.2M and are preparing for public launch after a successful closed alpha.

The Role

You'll own Fact Machine's trading infrastructure. This means building the orderbook, matching engine, trading APIs, and real-time data streams that professional market makers and active traders depend on. You'll be the person who understands both how to build these systems correctly and why the design details matter for market quality.

You'll build:

Orderbook and matching engine with support for multiple order types

WebSocket APIs for order placement and market data streaming

Fee and rebate calculation systems for market maker incentives

Rate limiting, and abuse prevention

Real-time risk monitoring and circuit breakers

You'll own:

Trading infrastructure and architectural decisions

Performance and latency optimization

Integration points with our smart contracts and settlement layer

Support for market makers (unblocking API issues, answering technical questions) and working closely with them to understand their needs.

Documentation and developer experience for traders using our APIs

What we're looking for

Required:

2-5 years building trading systems, matching engines, or exchange infrastructure

Experience at a crypto exchange, prediction market, or trading firm's internal systems

Deep understanding of market microstructure: order types, matching logic, maker/taker dynamics

Strong backend engineering skills in Typescript and either Rust or Go.

You've actually talked to traders and market makers - you know what they care about

Ideal:

You've debugged race conditions, or wash trading edge cases

You understand why certain design decisions affect market maker profitability

You've thought about fairness in matching (time priority, pro-rata, etc.)

You know when latency matters and when it doesn't

You can explain complex market dynamics to teammates

Not required:

You don't need to be a trader yourself

You don't need PhD-level quantitative skills

You don't need to have built HFT systems (we're consumer-focused, not institutional)

Why this role matters

Most consumer apps don't need this level of sophistication. But we're building markets where:

Market makers need to profitably provide liquidity

Retail users need tight spreads and instant execution

The platform needs to scale to high volume without breaking

If we get the infrastructure wrong, market makers won't participate. If market makers don't participate, markets aren't liquid. If markets aren't liquid, the product doesn't work.

You're building the foundation that makes everything else possible.

What you'll learn

How to design markets that balance platform economics with trader incentives

Market maker economics and liquidity provision strategies

How prediction markets actually work in production (not just theory)

Product thinking around financial infrastructure

How to launch trading systems from zero to production

The team

You'll work with:

Mads (CEO): Deep in product and mechanism design.

Pranj (CTO): Fullstack engineer who doesn’t sleep.

Engineering team: 4 engineers

Product and marketing teams preparing for public launch

You'll be the senior engineer who owns the trading vertical. Your backend teammates will handle integration points, but you'll have huge oversight on trading infrastructure.

Logistics

Timeline: We need someone who can start soon and move fast