Lead Software Engineer

Numerai is building a self-improving hedge fund.

We run an institutional quant fund built from thousands of machine learning models. Increasingly, those models are built by AI, not people. This isn't a thesis we're pitching. It's already how Numerai gets smarter every day (see https://numer.ai).

The next step is to compound it. Numerai is forming a new team with one mandate: build AI that does quant research autonomously, all the time. Discover new features, build better risk models, improve how the fund turns thousands of signals into a portfolio.

Numerai is a small company with an unusual amount of leverage per person. No bureaucracy, no politics. Significant equity.

The Role

You own the entire technical surface of the team: the data platform that ingests all of the information in the world, the research platform our quants and AI systems run on, and the trading pipeline that turns the Meta Model into positions in the market every day.

You will set the engineering bar and then keep raising it. Reviewing designs, killing complexity, deleting systems, and making the calls about what we build versus buy versus ignore. You will be personally accountable for the reliability of a live trading system where a silent failure is a real loss.

You will work directly with the founder on technical strategy: where the compute budget goes, which research directions get platform support, and how we get more alpha per engineer than firms with a hundred times the headcount.

Requirements

• 7+ years of engineering at top-tier quantitative trading firms, with real production ownership of research or trading infrastructure
• Deep systems knowledge across the technology stack - infra, ML, CI/CD, ETL - and you engineer systems from first principles
• Fluent in Python, and comfortable dropping into a systems language when the profiler says to
• A track record of designing systems that other strong engineers were happy to work in years later
• Excellent written communication (design docs, specs, post-mortems). You can make a hard technical tradeoff legible to a non-engineer
• Extreme ownership. No task is beneath you and no outage is someone else's problem
• Judgment about scope: you've seen how big firms overbuild, and you know which of their practices are load-bearing and which are theater

Nice to have

• Experience with ML training and deployment at scale
• Exposure to global equity trading, execution, or portfolio management systems
• Experience being the technical center of gravity on a team of fewer than 20 people