Financial 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.

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 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; we expect you to know exactly what you want and to communicate it clearly, not to spend your day writing production code.

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.

• 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.
• Help bring new strategies and venues online: test execution, validate connectivity, and give the green light for live trading.

We are actively expanding into strategies that carry proven traditional-finance approaches into digital and tokenized markets. Relevant backgrounds include fixed income and rates, credit, commodities, FX, and equity or futures relative value, applied to areas such as tokenized treasuries and money-market instruments, on-chain credit and private credit, basis, carry, and cash-and-carry arbitrage, and yield and funding strategies that span CeFi, DeFi, and RWA venues. If you have run TradFi strategies and want to apply that edge in digital and tokenized markets, we want to talk.

• Direct experience trading in a live, capital-at-risk environment: prop trading, market making, hedge fund execution, or a systematic trading desk.
• For digital assets, that means spot and derivatives venues (Binance, OKX, Deribit, Bybit, Hyperliquid), market microstructure, and funding dynamics. For TradFi and RWA candidates, deep fluency in your asset class and its microstructure, and a clear view on how it translates to tokenized markets.
• 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.
• Comfort using AI and machine learning as practical tools across research, execution, and workflow, including modern LLM and agentic tooling.
• The ability to work within, and improve on, existing execution and risk infrastructure while helping drive toward new markets and opportunities.
• An attributable, risk-adjusted track record (Sharpe, drawdown, capacity, and turnover).