Hiring nowInvestment, quantitative finance and market AI

AI Execution Researcher

Also called Execution ML Scientist

An AI Execution Researcher builds and tests models that decide how to trade orders under market and cost constraints. Done well, it lowers the cost of getting in and out of positions and keeps risk within agreed limits.

What they do

The work starts with a question such as how fast to work an order, where to place it, and when to stop. The researcher models price impact, spread, queue position and short-term signals, then turns that into a decision model. They clean market data, build backtests that reflect how orders really fill, and design controlled comparisons against the current approach. They tune the model with optimisation methods, check where it breaks, and explain results to traders and risk teams. They also monitor live behaviour and feed what they see back into the next version.

What a strong hire delivers

In the first months a strong hire delivers a working execution model and a controlled evaluation that shows how it compares with the existing approach. They set out the conditions under which it was tested and where it does not hold. They leave behind a backtest the team trusts, clear measures of realised cost and risk, and a sensible plan for moving the model into live trading in stages.

How you can tell they are good

Ask them to walk through one execution model end to end. Strong people explain the market assumptions, how they modelled impact, and how they stopped the backtest flattering the result. They can say what would make them switch the model off. Be wary of anyone who quotes only simulated gains, ignores fill realism, or cannot separate skill from luck in a noisy test.

Why it is hard to fill

Titles vary widely. The same work sits under execution researcher, quant researcher, trading strategist or ML scientist, and the label tells you little. The real scarcity is people who combine microstructure knowledge with modern machine learning and disciplined evaluation. Most candidates have one half. Quant finance people may be light on modern modelling, while ML people often underestimate market frictions and how misleading a backtest can be.

Where to find them

Look in systematic trading firms, brokers and banks with electronic execution desks, asset managers with in-house trading, and market makers. Typical titles include quantitative researcher, execution quant, algorithmic trading developer and trading analytics lead. Strong candidates also come from statistics, operations research and applied physics, where they have worked on noisy time series and optimisation under constraints.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an AI Execution Researcher

  1. Describe an execution model you built. What decision did it make and how did you know it worked?

    A strong answer names the decision, the inputs, the benchmark and the test design. They report cost and risk, not just average gains. Worry if results come only from a backtest with no thought for fills or impact.

  2. How do you model price impact when you only have historical data, and where does that estimate go wrong?

    Good answers cover temporary and permanent impact, the bias from your own past orders, and the limits of extrapolating to larger sizes. They admit uncertainty. Worry if impact is treated as a fixed constant.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are looking for an AI Execution Researcher to improve how we trade. You will build models that decide how our orders are worked in the market, and test them carefully against real costs and risk limits. You will work closely with traders, engineers and risk.

What you will do

  • Build models that guide order placement, timing and sizing under cost and risk constraints
  • Estimate price impact, spread and fill behaviour from market data
  • Develop realistic backtests and simulations of execution
  • Design controlled evaluations of new models against current methods
  • Measure realised execution cost and risk and report clearly
  • Work with traders and engineers to move models into live use in stages
  • Monitor live behaviour and improve models over time

What you will bring

  • Shown work building or testing execution or trading models
  • Working knowledge of market microstructure
  • Strong probability, statistics and time-series modelling
  • Experience with optimisation methods
  • Evidence of validating models and avoiding misleading backtests
  • Clear communication with traders and risk teams

Nice to have

  • Experience with reinforcement learning or learned policies in trading
  • Work with high-frequency or order book data
  • Experience in model validation or risk sign-off

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Financial model validationAdvancedChallenge financial-model assumptions and limitations
  • Time-series modellingAdvancedModel temporal dependencies and forecasts
  • Probability and statisticsWorkingQuantify variability and statistical evidence
  • Optimisation and operations researchWorkingChoose actions subject to objectives and constraints

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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