Quant Research Engineer – AGI Startup Trading – Remote up to £200k
Do you have deep expertise in Bayesian statistics and probabilistic modelling?
Can you design experiments in dynamic, partially observable environments?
Do you want to help build a system that continuously learns from the world to drive profitable market decisions?
The challenge
We’re an under 2-year-old VC-backed startup with $30m funding, building the most accurate model of the world. Our mission is to create prediction systems that evolve with shifting environments, ingesting information at scale and allocating capital with maximum efficiency.
We’re developing a trading platform that combines:
1. Prediction Market Frameworks – absorb streams of global data, produce evolving forecasts, and reconcile them into the most reliable view of reality.
2. AI Simulation Arenas – synthetic environments where predictive agents are tested against each other under varying constraints, enriched with partial real-world inputs.
3. Automated Fund Systems – infrastructure to generate hypotheses, attach probabilistic confidence levels, and execute trades accordingly.
4. Collaborative Hypothesis Pipelines – scalable systems that enable both humans and AI agents to contribute, test, and refine hypotheses continuously.
5. Next-Generation Prediction Markets – innovative market structures for gathering belief data from experts, AI systems, and digital replicas, spawning thousands of specialised predictive agents.
You’ll play a critical role in designing probabilistic frameworks and translating predictive insights into trading strategies.
About you
You’re a researcher at heart but pragmatic about execution. You’ve worked with advanced statistical models and are excited about applying them in real, profit-driven environments. You’re motivated by hard problems at the intersection of theory, data, and markets.
What we are looking for is someone with:
6. Deep expertise in Bayesian statistics, Gaussian processes, and adaptive modelling, with practical experience using tools such as Stan, PyMC, GPflow, scikit-learn, or TensorFlow Probability
7. Experience designing experiments in dynamic, partially observable environments, applying methods like multi-armed bandits, reinforcement learning (e.g. RLlib, OpenAI Gym, JAX-based RL frameworks), or probabilistic graphical models
8. The ability to translate predictive insights into market strategies, using approaches such as backtesting frameworks, Monte Carlo simulations, and portfolio optimisation libraries (e.g. QuantLib, zipline, backtrader)
9. A strong academic background, most likely a PhD in a technical field
Nice to have:
10. Publications in top-tier conferences or journals
11. Contributions to open-source projects
Benefits
This role is fully remote, with the core team based in San Francisco.
There’s a salary of up to £200k plus equity, along with typical startup perks.
Please note: You must be eligible to work in the UK, US, or EU to be considered.
Interested?
If this sounds like you, get in touch by clicking the ‘apply now’ button or reaching out directly:
12. Email: mo@mbnsolutions.com
13. Call: 02037736498