Almost every trading firm we work with is now hiring for "AI" or "ML" roles. The titles cluster around ML Engineer, ML Researcher, Applied Scientist and Quant ML Lead, but the work behind them varies a lot, and so do the people who succeed in them.
This is how we think about the split.
Production ML vs. Research ML
The two roles look identical on a job spec and almost nothing alike in practice.
Production ML engineers spend most of their week on infrastructure: feature stores, model serving latency, monitoring, retraining pipelines, the unglamorous work of getting a model from notebook to live trading. The people who do well here are strong software engineers first (distributed systems, low-latency C++ or Rust, profilers), with enough ML literacy to understand what the model is actually doing in production.
Research ML scientists spend most of their week doing what looks more like academic work: reading papers, running ablations, structuring experiments, writing internal memos. They tend to have stronger statistics / theoretical ML backgrounds, often with a PhD in ML, applied math or a hard science.
The mistake we see most: hiring one when the desk needed the other. A production engineer dropped into a pure research role gets bored and leaves in 6 months. A researcher dropped into production work fails to ship anything that survives contact with live latency budgets.
The fix is a spec that says plainly which one you need. We push on this during intake, because it saves everyone time later.
What gets hired vs. what gets ignored
Across roughly 30 ML/AI searches we've supported in the last 18 months, the skills that consistently move candidates to the top of shortlists:
- Real production deployment experience. Anyone who has actually shipped an ML model into a live PnL stream, even a small one, has a clear edge over candidates whose work has stayed in notebooks.
- Latency awareness. Trading firms care about microseconds; most ML candidates are accustomed to pipelines measured in seconds or minutes. The ones who can talk credibly about quantising models, optimising inference, or designing the boundary between ML and non-ML execution stand out.
- Statistical rigour. Particularly in roles touching alpha research. The strongest candidates are sceptical of their own results, run honest ablations, and know what backtesting overfitting looks like in the wild.
Skills that rarely change the outcome, even though CVs lead with them:
- Generic LLM / GenAI experience. Trading firms with serious LLM use cases are rare. Most claims to "extensive GenAI work" amount to API calls.
- Kaggle competition rankings. Useful as a directional signal, not as a hiring criterion.
Comp ranges (rough, Amsterdam / London)
For senior production ML engineers at established prop firms, total comp now sits roughly:
- Mid-level (3–5 years): €140–200k all-in
- Senior (6–9 years): €220–340k all-in
- Lead / Staff: €350–500k+ all-in
Research ML scientists trend slightly higher at the top end, particularly when there's a clear PnL attribution path. Crypto-native firms add another 20–30% across the board for the right hire.
These numbers move quickly. If you're hiring or thinking about a move, get in touch and we'll give you the current read on your specific level and seniority.
