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Founding ML Engineer
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Founding ML Engineer
Early-Stage VC Fund | London preferred (remote in Europe considered)
Compensation: Up to £200k base + carried interest
About
The fund has about 15 years of data on founders, companies, deals, and talent. Currently, this data is spread across a CRM, a talent tool, meeting notes, and calendars that don't communicate with each other, so the investment, talent, and operations teams each see different parts of it.
They aim to transform all of this, along with public signals like GitHub, arXiv, and people leaving top labs, into a system that helps them identify potential founders earlier. Questions like: Which individuals in their network are beginning to resemble future founders? Which teams have unusually strong concentrations of talent? Which weak signals are growing stronger over time?
Most of this will involve ranking, and the solution may not always be an LLM. It could involve classic ML, graph features, embeddings, learning-to-rank, or a combination of all of them. Proving that any of these methods work is a significant part of the job too—it's easy to build something that explains past winners but can't find the next one.
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This is a small team and has been one of the best-performing early-stage funds for a long time. There's no ML team or roadmap yet, so you'd be setting the direction and building it.
You'll work directly with the partners, ideally from London so you can discuss ideas in person, and share in the fund's carry.
What you'll do
- Build ingestion across GitHub, arXiv, Hugging Face, company registries, and internal CRM data
- Merge each founder's traces into a single profile, with provenance
- Map people, companies, papers, and repositories as a knowledge graph
- Turn partner feedback into features and ranking, classification, and anomaly-detection models
- Use bandits or RL to learn what to surface and where to look next
- Backtest the system on 15+ years of real bets and misses
- Auto-generate cited briefs on strong signals
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What you'll need
- Shipped ranking, classification, or time-series models on imbalanced data
- Built vector search and RAG over messy, multi-source data
- Record linkage at scale
- Reliable ingestion from APIs, crawls, and bulk dumps; strong Python and SQL
- Depth in productising LLMs, agents, and tool calling
- Fluency in backtesting, leakage, survivorship bias, and base rates
- Run a system end-to-end as the sole engineer
Optional Bonus
- Learning-to-rank, bandits, or RL
- Neo4j or similar for modelling people and organisations
- Entity resolution across languages
Part research, part production: hypothesis, experiment, evaluation, shipped system. You'll explain trade-offs plainly to non-technical partners.
Shortlisted candidates will be contacted within 48 hours.
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Jessica, London
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