Quantitative Trader

AlgoQuant Asset Management is a multi-strategy digital asset manager allocating capital across 25+ internal and external quantitative trading pods. Founded in 2018, we’ve grown into an institutional platform that pairs real trading edge with strong governance and advanced technology, serving family offices and institutional investors globally.

We are scaling deliberately: adding standout individual traders, backing established pods with capital and infrastructure, and building out strategies that bridge traditional finance and digital assets, including the fast-growing real-world asset (RWA) space. AI runs through how we operate, from research and signal generation to execution and monitoring.

This is a single, open search across two tracks. We want the best market operators we can find, whether you trade solo or lead a team.

• Individual traders who want real ownership of live risk, a direct line to research and engineering, and a genuine voice in how we trade.
• Pods and teams with a coherent, attributable track record who want institutional capital, prime and venue access, execution infrastructure, and strong governance behind them, whether through a seeded-capital arrangement or a full lift-out.

You’ll sit right at the intersection of live execution and quantitative research. You’ll manage live positions across spot, derivatives, structured products, and increasingly tokenized real-world assets, working closely with researchers and engineers to see our models perform in the real world, not just on paper.

This is a role for people who love the markets, think in data, and want real ownership over how capital moves, from model logic to risk limits to venue and counterparty relationships.

Traders lead development here. You own the trading direction and the requirements; our developers and engineers own the technical build. The logic is simple: you stay focused on the markets and the trade, while they turn your requirements into fast, reliable infrastructure. We expect you to know exactly what you want and to communicate it clearly, not to spend your day writing production code.

AI is central to how we operate. We use machine learning and modern AI tooling across research, signal generation, execution, and monitoring, and we want traders who treat AI as a practical edge and push us to use it better.

What you’ll do:

• Execute and manage live positions across spot, perpetuals, options, and structured products on digital asset venues.
• Monitor open positions, Greeks, funding rates, and cross-venue exposure in real time, and act fast when it matters.
• Dig into execution quality, slippage, market impact, and fill rates, and turn what you find into real improvements to our models and infrastructure.
• Set the requirements for the models and execution tooling you need, and work closely with developers to build them, so your time stays on trading.
• Put AI and machine learning to work across signal generation, execution quality, and monitoring, and help shape how the desk adopts new tooling.
• Partner with quant researchers to turn signals and strategy logic into sharp, capital-efficient trading plans.
• Build strong relationships with exchanges, prime brokers, OTC desks, and counterparties.
• Help bring new strategies and venues online: test execution, validate connectivity, and give the green light for live trading.
• Contribute to our execution infrastructure, tooling, and real-time monitoring dashboards.

Strategy areas we’re investing in:

• Digital assets (core). Spot and derivatives across major centralized venues, on-chain markets and DeFi execution, market-neutral and directional systematic strategies.
• TradFi and RWA crossover (named specialism). We are actively expanding into strategies that carry proven traditional-finance approaches into digital and tokenized markets.

What we’re looking for:

• Direct experience trading in a live, capital-at-risk environment: prop trading, market making, hedge fund execution, or a systematic trading desk.
• Strong knowledge of your market. For digital assets, that means spot and derivatives venues, market microstructure, and funding dynamics.
• Comfort with options and derivatives, including Greeks, vol surface dynamics, and hedging in fast-moving markets.
• A quantitative mindset, comfortable using Python for execution analysis, strategy monitoring, and tooling, with experience in order management systems and execution APIs.
• A clear sense of what you want from your models and tools, and the ability to brief and direct developers to get it built.
• Comfort using AI and machine learning as practical tools across research, execution, and workflow.
• The ability to work within, and improve on, existing execution and risk infrastructure while helping drive toward new markets and opportunities.
• Clear, confident communication, especially when things move fast.
• Experience with on-chain markets and DeFi execution is a strong plus.
• For senior candidates and pod leads, a live, attributable track record including P&L ownership and measurable execution alpha.

For pods and teams:

If you’re bringing a team, we’ll also want to understand a few specifics so we can move quickly:

• Team size, roles, and how long you’ve traded together.
• Strategy capacity, target markets, and the infrastructure you run on today.
• An attributable, risk-adjusted track record.