Senior AI Engineer

Type: Full Time Location: Remote (global), async-friendly Team: Quant and AI Reports to: Head of Engineering Compensation: Market-competitive base and equity

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About the Company

A well-funded team building institutional-grade clearing infrastructure for digital asset markets, connecting on-chain settlement to the trading firms that use it. Fully remote and globally distributed.

The Role

You'll build the intelligence layer of the clearing platform: the models and systems that forecast risk, detect anomalous market and counterparty behaviour, optimise margin and liquidation policy, and give both our team and our clients sharper, faster decisions.

You'll also build the internal AI tooling and agentic workflows that let a small senior team operate like a much larger one.

This is a hands-on engineering role with a strong quantitative core, not a pure research seat.

What You'll Build

• Risk and market models. Volatility and liquidity forecasting, scenario and stress generation, and counterparty-risk signals feeding the margin engine.
• Anomaly and manipulation detection across cross-venue market and on-chain data.
• ML-assisted optimisation of margin parameters, collateral haircuts, and liquidation policy, validated against rigorous backtests.
• Internal LLM and agentic tooling. Research copilots, monitoring agents, and automation that compounds the team's leverage.
• Serving and evaluation infrastructure to run models in production with reproducibility, monitoring, and clear correctness boundaries.

What We're Looking For

• 6+ years in ML and AI engineering with models actually shipped to production, not just notebooks. Finance, trading, or another quantitative domain is ideal.
• Strong Python, and comfortable writing performant Go or Rust for serving and data-adjacent code.
• Solid statistical and quantitative foundations: time series, probabilistic modelling, and an instinct for when a model is wrong.
• Practical experience with modern ML and LLM tooling, including building agentic or retrieval-augmented systems.
• Healthy scepticism about model outputs, and discipline around evaluation, backtesting, and guardrails in high-stakes settings.

Nice to Have

• Background in quantitative finance, market microstructure, or derivatives risk.
• Crypto and on-chain data experience.
• Familiarity with formal methods, or an appetite to reason about model behaviour with the same rigour we apply to protocols.

Benefits

• Market-competitive base salary and equity
• Top-of-market benefits
• Home-office stipend
• Fully remote