Founding Research Engineer

Event Horizon Labs; California (MO); On-site; USD 110,000 - 160,000; Full time

Job summary

Event Horizon Labs in San Francisco is seeking a Founding Research Engineer to build autonomous research infrastructure from scratch. This role involves designing systems for model training, research orchestration, and quantitative finance.

In a tight-knit team, you'll work at the cutting edge of ML systems to generate alpha and contribute to competitive salary and meaningful equity for founding members.

Qualifications

• Strong experience in Python and PyTorch.
• Proven expertise in research orchestration and quantitative finance.

Responsibilities

• Design a research orchestration layer to explore and evaluate hypotheses.
• Train models that power automated trading systems.
• Develop backtesting frameworks for evaluating trading strategies.

Skills

Python

PyTorch

Research orchestration

Quantitative finance

Job description

Founding Research Engineer

Event Horizon Labs — San Francisco (in-person)

We're a small, tight-knit team building autonomous research infrastructure from scratch. The orchestrator, the agents, the compounding knowledge base are already discovering profitable strategies and scaling with each new model release. These are founding roles with competitive salary and equity.

The next breakthrough isn't a better model — it's infrastructure for autonomous problem-solving. Markets are the first domain because feedback is immediate, signal is verifiable, and outcomes are directly monetizable. What we're building doesn't stop at markets.

Team from Citadel, Jump Trading, Stanford, Caltech, Berkeley.

You build the system that generates alpha. Design the research orchestration layer — what to explore, how to evaluate it, what to remember — and train the models that power it. You'll work at the intersection of ML systems and quantitative finance, closing the loop from hypothesis to P&L.

You might work on

• Research orchestration optimized for maximum exploration velocity
• Training small transformers on proprietary data for signal prediction
• Backtesting frameworks with regime-aware evaluation
• Knowledge systems that compound across thousands of sessions

Details

• Stack: Python, PyTorch, proprietary feature discovery, model fitting and backtesting infra
• Location: San Francisco (in-person)
• Comp: Competitive salary + meaningful founding equity